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Effects of Cochlear Implants on Children's Reading and Academic Achievement

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Abstract

This article presents a critical analysis of empirical studies assessing literacy and other domains of academic achievement among children with cochlear implants. A variety of recent studies have demonstrated benefits to hearing, language, and speech from implants, leading to assumptions that early implantation and longer periods of implant should be associated with higher reading and academic achievement. This review, however, reveals that although there are clear benefits of cochlear implantation to achievement in young deaf children, empirical results have been somewhat variable. Examination of the literature with regard to reading achievement suggests that the lack of consistent findings might be the result of frequent failures to control potentially confounding variables such as age of implantation, language skills prior to implantation, reading ability prior to implantation, and consistency of implant use. Studies of academic achievement beyond reading are relatively rare, and the extent to which performance in such domains is mediated by reading abilities or directly influenced by hearing, language, and speech remains unclear. Considerations of methodological shortcomings in existing research as well as theoretical and practical questions yet to be addressed provide direction for future research.

This article reviews the available evidence concerning the effects of pediatric cochlear implantation on the development of reading skills and academic achievement among deaf children. Literature reviews of this sort usually serve as introductions to empirical studies or as syntheses of evidence intended to support particular theoretical or philosophical positions. Neither of those situations is the case here. Rather, this article came about after the authors obtained two sets of empirical findings, studies initiated for very different purposes, which contradicted some generally accepted assumptions about the impact of cochlear implants on deaf children's academic achievement.

One of the investigations was conducted at the Western Pennsylvania School for the Deaf (WPSD). Like other schools for the deaf, WPSD has seen its enrollment of children with implants increase steadily; in 2006, children with implants accounted for approximately 20% of its total enrollment. Since 2002, WPSD has tracked the academic progress of its students with implants in order to determine how well they are able to serve those children (Rhoten & Marschark, 2003, 2006). Each student with an implant has been matched with a student without an implant on the basis of birth date; gender; and enrollment in academic, transition, or applied studies programs. Students have been compared on the basis of their scores on the Stanford Achievement Test, 9th Edition (SAT9), and the Pennsylvania System of School Assessment (PSSA). The SAT9 includes subtests for language, mathematics problem solving, mathematics procedures, and reading comprehension. The PSSA includes subtests for mathematics, reading, and writing. The results are easily summarized: In none of the 4 years evaluated has there been any significant difference between the implant group and the matched comparison group on any of the seven subtests. What trends have been observed are just as likely to favor the nonimplanted group as the implanted group. Further, a 2005 analysis including 17 children with implants and their matched peers indicated no significant correlations between achievement and either length of time with an implant or age at implantation (see below).

The second investigation was an examination of deaf college students' learning/academic achievement as it related to a variety of demographic and communication variables. Fabich (2005) reanalyzed data available from a series of experiments concerning classroom learning via sign language interpreting (see Marschark, Sapere, Convertino, & Seewagen, 2005, for a review). The experiments included 509 deaf students (with hearing comparison groups) enrolled at Rochester Institute of Technology (RIT), either in the National Technical Institute for the Deaf or in one of RIT's seven other colleges. Experiments involved interpreted lectures provided by RIT faculty members on science-related topics. Learning was evaluated using multiple-choice tests designed in collaboration with the instructors. In one analysis, Fabich examined relations of cochlear implants and learning among 35 deaf students with implants and 35 randomly selected students with hearing aids. No significant difference in learning was observed. In a second analysis, Fabich examined nine entrance and placement test scores available for deaf students enrolled at RIT during the 2004-2005 academic years. Scores were available for 83 students identified as using cochlear implants, and they were compared to 71 randomly selected peers with hearing aids. No significant differences were observed on any of the tests.

Although lack of power may explain the WPSD findings, it is unlikely to be an explanation for the failure to observe differences in the RIT data. Implicit in both studies, however, was the assumption that deaf students with implants should be performing at a higher level than peers without implants. In order to determine the validity of that assumption, the following review was undertaken. In addition to an examination of the literature with regard to academic achievement in general, special attention was devoted to research concerning the development of reading skills of children with implants. In part, this focus not only reflects the ongoing challenge faced by deaf students in acquiring literacy skills comparable to their hearing peers ( Marschark, 2007; Traxler, 2000) but also mirrors the concentration of empirical studies on the issue in the scientific literature.

The initial gathering of references for this review included all the relevant investigations that could be identified through academic and lay search sites, including those that are most frequently cited in secondary sources. The review covers primarily published (peer reviewed) research, although several other relevant studies are cited. Papers that failed to provide sufficient methodological or statistical information to fully evaluate their findings were considered, but these largely have been omitted unless they are of particular importance.1 Given the considerable heterogeneity observed among deaf children across a variety of domains, studies involving small samples were included only when key information concerning participant characteristics was provided.

Cochlear Implants and the Development of Reading Skills by Deaf Children

With the increasing number of children receiving cochlear implants and related improvements in hearing, language, and speech, there has been an expectation that enhanced phonemic awareness and phonological processing skills will result in better reading abilities among those children relative to peers without implants (e.g., Geers, 2002; Johnson & Goswami, 2005; Nicholas & Geers, 2006; see Spencer & Marschark, 2003, for a review). Several large-scale projects conducted at major implant centers in the United States and the United Kingdom have been most visible in providing evidence on this issue, but other important investigations have been reported as well.

"Implant Camp" Studies

A series of studies conducted by Geers (2002, 2003, 2004) took advantage of a summer camp designed to allow testing of large numbers of children with cochlear implants. These studies reported outcomes from 8- to 9-year-old children, all of whom had received cochlear implants by age 5 years. Geers (2002) reported data on 136 children, tested 4-6 years after cochlear implantation. The Reading Recognition and Reading Comprehension subtests from the Peabody Individual Achievement Test provided assessments of reading skills (responses were spoken and/or signed), and the Word Attack subtest from the Woodcock Reading Mastery Test provided grade-equivalent scores for phonic and structural analysis skills (responses were spoken). Phonological coding skills were assessed using the RHYME rhyme recognition task in which cards are sorted into pairs that "sound alike" and "do not sound alike." A lexical decision task was included in which half the nonwords were homophonic to a corresponding real word (e.g., word, werd) and half were nonhomophonic.

Geers found that better reading scores were associated with age (9-year-olds better than 8-year-olds), age of hearing loss onset (better among children who became deaf later), and higher nonverbal intelligence. Among the implant-related characteristics, duration of SPEAK software use, number of active electrodes, and dynamic range were all associated with higher reading scores. In contrast to most findings concerning hearing and language abilities (e.g., Archbold, Nikolopoulos, & O'Donoghue, 2006; Connor, Hieber, Arts, & Zwolan, 2000; see Marschark & Spencer, 2006), age of implantation was not associated with better reading scores (i.e., younger is normally better). Variables related to speech therapy, parent involvement, and private versus public schooling also were unrelated to reading skill, but children who were in mainstream programs and used spoken language were found to be better readers. Geers concluded that an emphasis on spoken language is an important educational choice for children who receive their cochlear implants early.

Findings of this sort do not allow assessment of whether it is children's postimplantation language orientations or their early (preimplant) language skills that are responsible for their reading success because the children who performed better had later hearing loss onsets and hence longer (pre-hearing loss) exposure to spoken language. A similar caveat could be argued with regard to Geers's finding that enrollment in mainstream settings was associated with higher reading scores relative to enrollment in separate programs for deaf children, because random assignment to different school placements is not feasible for such studies (Marschark, 2007, pp. 49-52).

Geers (2003) reported word reading and comprehension data for 181 children who were 8-9 years old, noting that over half scored within the average range for their age relative to normative data for hearing children. Reading competence was positively associated with nonverbal intelligence, family socioeconomic status, language skills, and early auditory experience (i.e., later onset hearing loss and implantation). Geers (2004) also reported testing 181 children (presumably the same as those in the 2003 study) and presented data from 133 children with congenital hearing losses and performance IQ scores of 80 or greater. Another sample of 39 children with deafness acquired by age 3 years was also described. Neither age of implantation nor duration of implant use was found to be related to reading comprehension, findings replicated by Willstedt-Svensson, Sahlén, Mäki-Torkko, Lyxell, & Ibertsson (2005; cf. Sherman & Cruse, 2004).

Although it is sometimes assumed that earlier implantation per se leads to better outcomes, the Geers studies point out that this association is mediated by both the extent of auditory experience prior to hearing loss and the length of time between hearing loss and implantation, factors frequently acknowledged but rarely examined in relevant studies. Her results indicate that later onset hearing losses (and thus later implantations) are associated with better reading ability, presumably because those children have greater language experience to support their learning to read. Alternatively, the failure to find an inverse relation between reading and age of implantation might simply indicate that the link between language and reading is an indirect one (see Connor & Zwolan, 2004, below).

In a postscript to the previous three studies, Geers (2005) reported an examination of reading levels of each child in a subsample of twenty-six 8- to 9-year-old children with implants, also involved in the summer camp testing program. Follow-up data were obtained when the children were in high school. Those data revealed that although, as a group, the children were reading on grade level (mean grade level 3.4) when they were 8-9 years old (in Grades 3-4), by the time they were 15-16 years old, they were an average of almost 2 years behind grade level. Those 9th and 10th graders had a mean grade level of 7.7, with 10 of 26 reading at or above grade level 9.5. Geers reported that 16 of the students (61% of the sample) scored within the average range for hearing age-mates, but the data from the individual students indicated that impressive early reading achievement for children with implants does not necessarily maintain over time. This may be a result of the fact that around the fourth grade (in the United States), we stop teaching children to read and expect that they will read to learn. Such findings thus may reflect a lack of explicit instruction and/or the greater vocabulary demands of books for older children (Wauters, Tellings, van Bon, & van Haaften, 2003) rather than anything specific to language development per se. Nevertheless, such data are a reminder that cochlear implants are helpful for many if not most deaf children learning to read, even if they do not completely level the playing field with hearing age-mates. The language and cognitive abilities underlying reading are complex, and the link between language and reading is not necessarily a direct or simple one.

Relations of Language, Reading Achievement, and Cochlear Implantation

The large-scale studies by Geers provided evidence that greater exposure to spoken language communication during the school years was associated with enhanced reading abilities, but her analyses indicated that other factors may have been more important. Connor and Zwolan (2004) examined the possibility of an indirect link between language and reading abilities in a study involving 91 children using cochlear implants. Their pool was reported to have an average age of 11 years, and all had used their implants for at least 4 years at the time of evaluation. Ten children received their implants prior to age 3 years, 40 at age 5 years or younger (14 between 3 and 4 years), and 51 after age 5 years (32 prior to 9 years, and 19 between 11 and 14 years of age). All attended regular local schools and used either spoken language or total communication in educational settings. Connor and Zwolan assessed preimplant vocabulary using the Picture Vocabulary test of the Woodcock-Johnson Test of Cognitive Ability and the Expressive One-Word Picture Vocabulary Test. After implantation, reading comprehension was evaluated using the Passage Comprehension subtest of the Woodcock Reading Mastery Test-Revised. Children gave their answers using signed and/or spoken language.

Connor and Zwolan (2004) did not report the reading levels of the children involved in their study, so there is no way to know how they compared with those of deaf children without implants or hearing age-mates. In contrast to the results obtained by Geers (2004), they found that earlier implantation was associated with higher reading comprehension scores, and communication method did not directly affect reading comprehension scores when other variables were controlled. Length of implant use was negatively related to reading comprehension, suggesting the attenuation of implant-related facilitation in reading among older children ( Ibertsson, Vass, Árnason, Sahlén, & Lyxell, 2006). Similar results can be found in data reported by Johnson and Goswami (2005) and Geers (2005).

Johnson and Goswami (2005) examined the phonological awareness skills of children who had received cochlear implants prior to age 3.2 years, children who had received implants after age 3.8 years, and comparison groups of deaf children who used hearing aids and reading age-matched hearing children. Tasks examined awareness of rhyme and phonemes as well as speech-reading (Test of Adult Speechreading word subtest). Vocabulary development, rhyme awareness, and speech-reading all were positively related to reading ability, but there were no differences among the three groups of deaf children in either reading ability or phonological awareness. Assuming that the children with hearing aids had hearing losses comparable to the children with cochlear implants (not reported), these results suggest that there are alternative routes to reading other than or in addition to skilled phonological processing ( Connor & Zwolan, 2004; Leybaert, 1993), at least for children who receive their implants early. The generality and reliability of such results, however, remain to be determined.

Archbold et al. (2006) examined the effect of age of implantation on reading ability in a study including 105 children who received cochlear implants between the ages of 1.3 years and 6.9 years ( M = 4.1 years). Five years after implantation, the children had achieved reading levels ranging from 4.3 years below grade level to 2.6 years above grade level ( M = 0.9 year delay). Conducting another follow-up study 7 years after implantation, Archbold et al. found an overall mean reading delay of 2.2 years. More importantly, there were significant effects of age of implantation at both points of test. At the 7-year follow-up, 100% of the children implanted between 6 and 7 years of age were reading more than 1 year below grade level. Among those implanted between 4 and 5 years, 81% were reading more than 1 year below grade level. Only 44% of children implanted between 1 and 3 years of age were reading more than 1 year below grade level, however, whereas 46% were reading within 1 year of grade level and 10% were reading more than 1 year ahead of grade level. Archbold et al. emphasized the importance of early implantation to reading achievement, but, like ( Geers, 2004), also pointed out that cochlear implants do not guarantee reading success. 2

Moog (2002) conducted a study involving 17 children with cochlear implants between 5 and 11 years of age who attended an intensive oral program that provided focused instruction in reading. The children had been implanted between 2.4 and 7.7 years ( M = 4.2 years) and were tested when they were between 5.8 and 11.8 years of age. Fifteen of the children had prelingual hearing losses (12 congenital). The children were administered the Phonetically Balanced-Kindergarten word lists to test speech perception, the Picture Speech Intelligibility Evaluation to test speech intelligibility, the Peabody Picture Vocabulary Test (third edition), as a measure of perceptive vocabulary, and the Expressive One-Word Picture Vocabulary Test as a measure of expressive vocabulary. They also were tested using the Clinical Evaluation of Language Fundamentals (CELF-3) to examine perceptive and expressive language skills and the Gates MacGinitie Reading Test (third edition) and the SAT9 to assess reading. Moog reported neither any statistical analyses, comparisons with other groups of children, nor any relations between language and reading. The only reference to reading achievement was that more than 70% of the students (apparently, 12 of the 17) scored within the average range for their age. Moog concluded that "These data demonstrate what is possible for deaf children who benefit from a combination of a cochlear implant and a highly focused oral education program" (p. 138).

The Iowa Studies

Although clinicians usually emphasize the importance of spoken language skills for children with cochlear implants, several studies from a research group at the University of Iowa have evaluated the reading abilities and academic achievement of children with cochlear implants who were exposed to both sign language and spoken language. Most of those children have received support from sign language interpreters throughout their school years and appear to use sign and speech (not necessarily together) in various contexts. Spencer, Tomblin, and Gantz (1997), for example, investigated the reading skills of 40 such children who had prelingual, profound hearing losses. At the time of testing, the children were aged 6-17 years ( M = 11.17 years), had received their implants between the ages of 2 and 13 years (cf. Archbold et al., 2006), and had from 2 to 9 years experience with their implants ( M = 5.3 years). Note that this contrasts with the Geers studies, insofar as the children all had prelingual hearing losses but did not necessarily have their implants prior to age 8 years.

Using the Woodcock Reading Mastery Test-Revised, Spencer et al. (1997) found the children to have a mean grade-level reading score of 4.13, although variability was quite high ( SD = 3.65) undoubtedly due to the large age range. The 28 children who were in grades 4-12 were reported to have achieved a mean grade-level reading score of 5.15 ( SD = 3.9). Approximately 25% of the children were reading at or above their grade levels and almost 20% within 8 months of their grade levels, but another 25% were reading at a level 30 months or more below grade level. Spencer et al. reported that 54% of the children between Grades 4 and 12 were reading above the fourth grade level. This compares with normative data from the SAT9, indicating that approximately 50% of deaf 18-year-olds read below the fourth grade level and 50% read above that level ( Traxler, 2000). 3 Spencer et al. did not report on relations among age of implantation, duration of implant use, language skills, and reading ability.

Tomblin, Spencer, and Gantz (2000) explored language development in a group of 17 children who had prelingual, profound bilateral hearing losses and received cochlear implants between 2.6 and 10 years of age. Reading acquisition was examined in a group of 30 children including "most of the children" (p. 302) who participated in the language study. Those children also had prelingual, profound bilateral hearing losses and received cochlear implants between 2.6 and 14 years of age. At the time of testing, children in the language study ranged in age from 8.5 to 18.4 years ( M = 12.8 years) and had experience with their implants ranging from 3 to 11 years ( M = 7.1 years). The Woodcock Reading Mastery Test-Revised and three language measures from the CELF were administered, with the reading evaluation administered to the group of 30 children twice, 1 year apart. At the time of the first evaluation (mean age 11 years, average grade level mid-fifth grade), the mean grade-level-equivalent reading score was mid-third grade. At the time of the second evaluation (mean age 12 years, average grade level mid-sixth grade), the mean reading level was mid-fifth grade, a 2-year improvement. In particular, the students who were nearing the end of high school were found to be "reaching average to above average reading levels, whereas the children in early grades [were] well below average" (p. 303, cf. Geers, 2005). Tomblin et al. (2000) concluded that there is a causal relationship between spoken language and reading, but explict relations between their language data and reading data were not examined.

A study by Spencer, Barker, and Tomblin (2003) involved another small sample: 16 children with an average age of 9.8 years. They had received their implants at an average age of 3.9 years and had an average length of experience with their implants of 5.9 years. All were enrolled in public school classrooms with sign language interpreting support and were reported to use simultaneous communication. The study included a comparison group of 16 hearing children with a similar mean age. Spencer et al. (2003) used the same reading and language assessments as Tomblin et al. (2000) and also elicited writing samples. Reading comprehension scores of the children with implants were significantly below those of the hearing children, but only by about 10% (99.5 vs. 90.1). Grade-equivalent scores indicated the children with cochlear implants to be reading at an average grade level of 3.3 years and the hearing children at 3.8 years. Note that although individual grade levels were not indicated, the reported average age would have put these children at around fourth grade. Ten of the 16 children with implants were reported to be within 1 SD of the average standard score according to available norms, and only two fell below 1.5 SDs of the average standard score. The deaf children also lagged behind their hearing peers in writing productivity, which was reliably correlated with language scores for the deaf but not the hearing children. Given the relatively better reading skills of the children with implants relative to typical findings with deaf children (e.g., Traxler, 2000), Spencer et al. concluded that literacy skills among children with cochlear implants are associated with gains in English language competence made possible by their implants.

Spencer, Gantz, and Knutson (2004) obtained similar results in a study involving 27 children consecutively implanted at a single center. All had prelingual hearing losses and received their implants between 2.4 and 12.7 years of age ( M = 6.4 years). At the time of testing, they had between 3 and 14 years experience with their implants ( M = 9.9 years). The children were tested subsequently either when they were in the 10th grade or at least 16 years of age. Of the 27 participants, 24 reported at least 7 years of consistent implant use, where "consistent" was defined as eight or more waking hours. Seven of the participants wore their implants only in school or at work. Another 10 students apparently discontinued use of their implants either during the first 3 years after implantation (three) or later (seven). 4 Admittedly, there was a large age range in this study, but it also involved consecutively implanted children, inclusion of inconsistent as well as consistent implant users, and a relatively long period of implant use for most children. Taken together, these factors make it an important investigation of the long-term effects of cochlear implantation on reading (see Beadle et al., 2005) and one likely having greater validity than many studies.

The inclusion of a number of late-implanted children suggests that significant reading delays would be likely in the Spencer et al. (2004) study, at least on the basis of the Archbold et al. (2006) findings. In fact, scores on the Woodcock Reading Mastery Test-Revised (with a standard score of 100 for hearing children) indicated that the entire group of children with implants had a mean score of 89 ( SD = 17) and those who were consistent users of their implants had a mean score of 92 ( SD = 17). The inconsistent implant users had a mean score of 77 ( SD = 7). Thus, those students who consistently used their cochlear implants generally were reading on par with hearing age-mates, and the group as a whole was well within normal limits (expected M = 100, SD = 15). The only apparent difference between this study and that of Archbold et al., aside from the small and diverse sample, was the fact that all the students in the Spencer et al. study used both sign language and spoken language, whereas most of those in the later study apparently used spoken language in school. 5

Reading Related to Other Aspects of Achievement

Three additional studies are relevant to the discussion of reading abilities of children with cochlear implants. Although each is quite different, they have in common a rather broad perspective on literacy skills in this population. Vermeulen, van Bon, Schreuder, Knoors, and Snik (2007) examined reading comprehension and word recognition as separate indicators of reading ability of children in The Netherlands. The study involved a comparison of 50 students with implants (aged 7-22 years) with data collected by Wauters, van Bon, and Tellings (2006) from 504 deaf students with hearing aids and a comparison group of hearing students. The mean age of hearing loss onset was 1.1 years ( SD = 1.6 years), and the mean age of implantation was 6.2 years ( SD = 2.3 years). All the children with implants had a minimum of 3 years implant experience, but there was a mean duration of auditory deprivation prior to implantation of 5.1 years ( SD = 2.8 years). Forty five of the 50 had prelingual hearing losses, most of them acquired. Vermeulen et al. (2007) found that educational placement prior to implantation was unrelated to reading scores (cf. Geers, 2002). Reading comprehension among the children with implants significantly exceeded that of children with hearing aids, but still lagged significantly behind hearing peers. Implants were associated with an advantage in word recognition relative to hearing aids only among secondary school students, not primary school students. Vermeulen et al. (2007) noted, however, that the participants in their study had been implanted relatively late (between 2 and 12 years, with a mean of 6.2 years), a difficulty also inherent in the Iowa studies.

Thoutenhoofd (2006) conducted a study involving 152 school-aged deaf students with cochlear implants drawn from the population of 1,752 deaf students in Scotland. Children ranged in age from 5 to 12 years ( M = 8 years), were an average age of 3 years old when they received their cochlear implants, and had an average of 4 years experience with their implants. Thoutenhoofd examined National Test assessments in several academic subjects (see below). With regard to the reading scores, he found that older students with implants were farther behind in their reading skills (for their chronological age) compared to younger students, a finding similar to that reported by Geers (2005). Students aged 11-13 years lagged behind their hearing peers in reading scores by approximately 3 years, whereas students aged 15-17 lagged by 4-5 years. Analyses controlling for age of implantation were not conducted, so it is unclear whether such results reflect a real widening gap in literacy skills for implanted children over the school years or a confound due to the fact that older children may have received their implants after a longer period of auditory deprivation ( Connor & Zwolan, 2004).

Crosson and Geers (2001) examined the narrative abilities of 87 children aged 8-9 years who had used their implants for at least 4 years, relative to 28 hearing children. Scores on the Reading Comprehension subtest of the Peabody Individual Achievement Test also were reported. Stories were prompted using an eight-picture sequence, and children's productions were scored on the basis of several discourse-related features. Children with better speech reception skills were found to produce better narratives, a finding consistent with the hypothesis that narrative abilities are acquired largely through incidental learning. Narrative skills also were related to reading. The children in grades 3-4 obtained an average reading level of grade 2.5. Scores were reported as ranging from grade level 0.1 to 8.3, but the SD was only 0.15, indicating that most of the children were reading at or below grade level. Using a discourse scoring system that emphasized use of conjunctions and particular referents, Crosson and Geers found that children with profound hearing losses were "deficient" in their narrative skills. That result contrasts with conclusions from research involving story grammars, in which deaf children aged 7-15 years who sign have been found to be comparable to hearing age-mates in their narrative abilities even without picture prompts (e.g., Marschark, Mouradian, & Halas, 1994). Nevertheless, the Crosson and Geers findings are consistent with the earlier results in indicating that children with better language production skills are also better narrators.

In summary, the above research indicates that children with implants frequently read better than deaf peers who utilize hearing aids, even if they lag behind hearing age-mates. The link between age of implantation and length of implant use is rather inconsistent, however, either reflecting the considerable heterogeneity of deaf children relative to hearing children (Marschark, 2007) or indicating methodological confounds. For example, children with later hearing losses, who thus likely have greater preimplant language skills, tend to show better reading achievement, but this confound is not addressed in most relevant studies. There are also several studies indicating that children with implants who have access to both spoken and sign language throughout their school years show higher levels of reading achievement. Those investigations have involved samples quite diverse with regard to age and age of implantation, however, and whether the findings reflect a contribution of simultaneous communication per se, the benefit of early access to language, or enhanced access in the classroom through the availability of both speech and sign remains to be determined.

Taken together, all the results discussed thus far emphasize a positive link between early expressive language skills and later language-related achievements by children with cochlear implants, whatever the modality of their preimplant communication ( Spencer & Marschark, 2003; Yoshinaga-Itano, 2006). Some caution is needed in this regard because children who have congenital or early-onset hearing losses also may be implanted earlier (and reported in the literature earlier), but they also will have lesser experience with their cochlear implants (see note 2). Further, studies by Geers (2005) and Thoutenhoofd (2006) indicate that grade-level reading abilities in younger children do not necessarily maintain through secondary school. Such findings may reflect the fact that deaf children with implants do not have hearing, language, and speech skills fully comparable to hearing peers, even if they fall into the normal range ( Yoshinaga-Itano, 2006). Most children with cochlear implants function like hard-of-hearing children, at best, and hence likely are at a disadvantage with regard to phonological processing and phonemic awareness as well as incidental learning from auditory information. Those disadvantages would be significantly less than what is experienced by peers without implants and comparable hearing losses, accounting for differences reported between children with implants and those with hearing aids, but they appear nonetheless sufficient to have specific impact on reading ability. Another possibility is that early gains in reading, at least at the group level, decline because some students opt out of speech services during their teen years, services that provided them with early advantages. Those services also may have been more intensive during younger years, when they were learning to read ( Wauters et al., 2003), and thus contributed to early reading success.

Before drawing strong conclusions about a link between the quality of auditory input and reading among children with cochlear implants, it is important to look at academic achievement more broadly. To the extent that cochlear implants fall short of providing deaf children with access to classroom information that is fully comparable to that of hearing peers, the pattern of results observed in assessments of reading ability may be more general than is supposed by those who focus on sound-level foundations of reading. This situation is akin to the seeming obsession of educators and investigators with deaf children's literacy skills, when challenges are observed across essentially all parts of the curriculum ( Jensema & Trybus, 1978; Marschark, Lang, & Albertini, 2002). It is only when we understand the whole functioning of deaf children in social, language, and academic domains that we will be able to fully support their needs and take advantage of their strengths.

Cochlear Implants and Academic Achievement of Deaf Children

As in the literature concerning the development of literacy skills by children with implants, it is not difficult to find implicit if not explicit suggestions that those children should demonstrate greater academic achievement than deaf children who rely on hearing aids or do not use assistive listening devices. Some of those assertions likely are based on the assumption that implants will provide greater access to information in the classroom, but few if any studies have examined deaf children's implant-aided perception of spoken language in real-world settings (O'Donoghue, 2006). There also appear to be assumptions that because implants facilitate literacy skills, academic achievement necessarily will be enhanced. Some evidence with regard to the effects of cochlear implants on academic achievement is available, but there appear to be several "red herrings" as well.

Results obtained by Summerfield (2004) 6 are a case in point. Several secondary sources have reported that Summerfield found children with implants to demonstrate superior academic achievement relative to children with hearing aids (e.g., Archbold, 2005). Although Figure 3 of that report is labeled "Children with implants achieve more," the data actually represent results from a survey of teachers who "estimated the abilities of children in reading, writing, and number work" (p. 3). The graph indicates that teachers rated deaf children without implants as showing steep declines in academic achievement as a function of degree of hearing loss, whereas children with implants were rated as performing at uniformly high levels of academic achievement across a wide range of hearing losses. Again, however, these were teachers' opinions, not actual measures of academic performance, and it is unclear how much exposure the teachers had to children with cochlear implants.

O'Donoghue (2006) and Beadle et al. (2005) criticized prior studies looking at achievement following cochlear implantation, as well as hearing, speech, and language studies more generally, for lack of careful experimental methods. They pointed out that the results of many studies are tainted by (a) the exclusion of low performers and children who have discontinued regular use of their implants (i.e., selection bias), (b) not including individual data on all children, (c) bias in testing protocols (i.e., over-reliance on laboratory tasks unrelated to real-life situations), (d) inadequate experimental follow-up, and (e) poor or at least limited experimental designs (i.e., lack of prospective and longitudinal studies). Beadle et al. therefore designed a study that was both prospective and longitudinal, involving a group of 30 consecutively implanted children. They followed all members of the group over a 10-year period after implantation with regard to "device use and function, speech perception and speech intelligibility outcomes, and current academic or occupational status" (p. 1152). The children in this unselected sample were all profoundly deaf prior to implantation at 2.5-11 years of age ( M = 5.2 years) and were implanted in the same center over a 4-year period. The only child excluded from the study was one whose implant was removed due to persistent middle ear infections and pain whenever the implant was stimulated. At the time of study, all the children had 10-14 years of implant experience.

Although many of the findings from the Beadle et al. (2005) study are beyond the scope of this article, it is interesting to note that after 10 years of implant experience, 26 (87%) of the 30 participants reported that they always wore their implant and gained considerable benefit from it. Sixty percent (18 of 30 participants) were able to use the telephone with a familiar speaker. Eight (26.7%) of the children, however, experienced device failures (and one child had two failures) and had to be reimplanted.

With regard to academic attainment, 19 participants were under the age of 16 years at the time of testing and attended academic secondary educational programs. Six of those were in schools for the deaf, seven in units attached to regular local schools, and six in regular local schools with support services ranging from a note-taker to an occasional visit from an itinerant teacher of the deaf. Seven of the secondary students were in programs where they used total communication and code switched regularly between speaking and signing, depending on their communication partners (see note 5); the other 12 used primarily spoken communication in the classroom. Ten of the 30 participants were attending college or university at the time of study, enrolled in a variety of academic and vocational programs, and four others were completing additional academic courses with the goal of entering college or specific employment. Half of them used spoken communication in academic settings, yielding 17 of the 30 original participants who exclusively used spoken language in academic settings. Specific levels of academic achievement were not reported, but with 29 of 30 students enrolled in secondary and postsecondary educational programs (one stayed home full-time with her two children), it appears that this group could be considered academically successful.

More specific evidence on academic achievement is available from two published sources. One is the study by Spencer et al. (2004) described above with regard to literacy. They also examined academic achievement using the science, social studies, and humanities subtests of the Woodcock-Johnson Tests of Achievement. As was the case for reading, Spencer et al. found that the "CI participants" (apparently those students who were consistent users of cochlear implants) performed comparably to hearing age-mates, achieving an overall mean score of 103.88 ( SD = 19.93), where the expected score for hearing children would be 100. Performance of students who did not use their implants was not reported. Spencer et al. also noted that 16 of the participants in their study had already reached the age of 18 years. Seventy-five percent of those young adults (12) were attending postsecondary institutions, two were homemakers, and two did not report their current status. Once again, this is a group of individuals who presumably would be considered academically successful.

The Spencer et al. (2004) study also is noteworthy because it is the only one available in which deaf children with implants have been found to be performing at a level fully equivalent to hearing age-mates in academic performance, and the students had utilized both sign and speech during the school years. There were only 27 children in the study with a fairly large age range, but the fact that the sample consisted of consecutively implanted children who were both inconsistent and consistent implant users gives considerable confidence in the validity of the results. The findings also emphasize that use of sign language with implants should not be anathema, although more research clearly is needed to clarify the effects on the academic achievement of spoken and sign language at different ages.

Thoutenhoofd's (2006) study of school-aged deaf students in Scotland, also described in the previous section, included evaluation of academic achievement in mathematics and writing as well as reading. He found that differences between deaf and hearing children in academic achievement were attenuated by cochlear implants, as students with profound hearing losses and implants functioned more like hearing aid users who had moderate losses in mathematics and like students with moderate to severe losses in writing as well as reading. Most deaf students with cochlear implants still performed below the national average across academic areas, however. Thoutenhoofd did not examine relations between language and achievement, but he reported that 79% of the students were exposed only to spoken English at home and 46% were exposed only to spoken English at school.

Interestingly, Thoutenhoofd examined the frequency with which children moved into and out of separate programs for deaf children. In contrast to the common assumption that children who receive cochlear implants are likely to move from separate programs into mainstream classrooms (M. Svirsky, personal communication, November 1, 2006), he found no such migration. Most of the students with implants at the outset of his study were in mainstream classrooms and stayed there. Of the 60 students originally in mainstream schools, 19 moved out of those schools into special placements intended for deaf students. In contrast, only six students moved from special placements to mainstream settings. Data of this sort are not available from most other studies (but see Vermeulen et al., 2007), so the question of the effects of cochlear implants on educational placement for deaf students with implants remains uncertain. It has been argued that cochlear implants and mainstream placements may be more economical than hearing aids and separate school placements for deaf children (e.g., Francis, Koch, Wyatt, & Niparko, 1999; O'Neill, O'Donoghue, Archbold, & Normand, 2000), but the issue of academic success for children with various characteristics remains to be empirically explored.

Discussion

Archbold (2005, p. 54) suggested that "cochlear implants may work too well." She noted that children with implants may appear to hearing teachers as having no hearing-related difficulties, and thus they also may appear not to require any support services in the classroom. "However, in typical noisy educational settings with increasingly demanding curricula, the child needs sensitive support, and monitoring of the functioning of this complex technical system they wear. At secondary or high school level, the implanted deaf child may have excellent intelligibility of speech but lack the sophisticated language to deal with complex curricula, particularly in noise and with changing teachers." Indeed, it appears a tacit assumption in the field-and certainly one taken home by many parents of deaf children-that the enhanced language skills typical of most deaf children who receive cochlear implants will allow them to function at the level of hearing peers in regular school classrooms.

The literature review above indicates that even if children with cochlear implants frequently surpass deaf age-mates who have hearing aids and similar hearing losses, implants do not guarantee reading and other academic skills comparable to hearing age-mates. Although young deaf children in some studies have been reported to be reading at age-appropriate grade levels, preliminary evidence suggests that there is later divergence, as those advantages are not necessarily maintained by all children, even among those who received their implants relatively early (e.g., Archbold et al., 2006; Geers, 2005; Ibertsson et al., 2006; Johnson & Goswami, 2005). "Early" in most studies, however, refers to ages 1-3 years, and it remains to be seen whether similar findings are obtained with children who are implanted within the first year. Given the findings thus far, however, one should not assume that academic differences between deaf children and their hearing peers will disappear if the former receive cochlear implants.

This caveat follows from several factors underlying learning by children with significant hearing losses ( Marschark et al., 2002). Most obviously, children with implants do not have hearing, speech, or language skills that are equivalent to those of hearing peers, and thus, they are likely to miss some amount of information in the classroom. A similar argument would apply to informal learning situations (both explicit and incidental) prior to the school years as well as during them. It may be that the advantage seen in studies by Tomblin, Spencer, and their colleagues for children who use sign language and speech in school with support from sign language interpreters is a consequence of those children having greater access to classroom discourse with two modes of communication rather than one. Children involved in the Archbold et al. (2006) studies also evidence considerable sign language skill, although that variable apparently has not been explored in their studies. Studies by Geers also include children with some amount of sign language skill, because descriptions of the methodologies report that children respond in spoken language and/or sign language, but again, that variable has not been specifically analyzed. Additional studies are needed in order to determine how the link between language modality and reading/achievement might interact with age at implantation, language skills prior to hearing loss, and postimplant fluencies in both signed and spoken language.

Research Needs Better Methodological Controls

Control for language fluencies before and after hearing loss and before and after cochlear implantation is one of the weaknesses observed in the reviewed literature. An advantage in reading ability for children who received their implants later was clearly evident, but only in cases where later implantation apparently entailed later hearing losses. An advantage for children who received their implants earlier was observed when it entailed earlier habilitation for congenital and early-onset hearing losses. Similarly, although some of the studies described above took into account pre- and postimplantation hearing losses in examining reading and academic achievement, most studies analyze language and achievement separately without controlling for language fluencies. Consideration of prior world and vocabulary knowledge is almost nonexistent (cf. Vermeulen et al., 2007), as is examination of preimplantation phonemic awareness or the phonological skills. Results from reading readiness tests thus would be interesting to include in future studies.

Such criticisms are not meant to fault particular studies but are intended as a call to recognize that there are considerable theoretical, practical, and social-emotional consequences of research that frequently only address part of the cochlear implantation story. Studies that lack comparison groups of children who use hearing aids (or not) and have hearing losses comparable to the children with cochlear implants typically refer to standardized norms for hearing children, but there appears to be little effort to ensure that the assumptions underlying those norms (e.g., comparable educational backgrounds, first language fluency) are met. Factors such as socioeconomic status, parental hearing status, early intervention experience, and others known to affect the development of deaf children are largely ignored. Several investigators have emphasized the importance of educational placement involving spoken language after implantation, but it is rare that information is provided as to whether those children had better spoken language skills prior to implantation, thus making them better candidates in the first place. Use of consecutively implanted experimental samples not only provides some remedy for such potential confounds but also creates greater variability due to the greater age ranges typically involved.

Beadle et al. (2005) raised the methodological concern that most studies of children who have received cochlear implants have included only consistent implant users. As a result, reported results of the beneficial effects of pediatric implants likely are somewhat inflated (O'Donoghue, 2006). Certainly, there is likely to be difficulty in contacting and recruiting the research participation of individuals who have discontinued use of their implants for indeterminate causes, sometimes with strong social-emotional consequences. Nonetheless, Beadle et al. and O'Donoghue have argued eloquently from within the cochlear implant professional community that more accurate information concerning implantation outcomes will only benefit the field.

Language, Learning, and Cochlear Implants

Claims that utilization of sign language might prevent children from fully utilizing the potential of the cochlear implants or might facilitate academic achievement remain to be validated. Clearly, the purpose of cochlear implantation is to improve hearing, and the expectation is that speech and language will not be far behind. (Issues of socialization, emotional development, and cognitive growth are beyond the scope of this review and are just now receiving empirical attention.) The threshold for the amount of auditory/spoken language exposure necessary in order for children to optimally benefit from a cochlear implant remains to be determined (Spencer & Marschark, 2003). Although there still has not yet been any demonstration that visual forms of communication will impede development of oral-aural forms of communication among children with cochlear implants, there nonetheless is a strong propensity in the field for emphasizing auditory information over potential sources of visual information for such children.

Research to date has provided strong evidence that pediatric cochlear implantation can provide many deaf children with significant advantages in reading and other academic domains. The extent to which these advantages stem directly from improvement in auditory reception, from enhanced ability to engage in face-to-face language interactions with a variety of individuals, and/or from indirect sources such as greater incidental learning and cognitive growth is unclear. Rather than assuming that achievement necessarily will follow enhanced hearing and language skills, it is essential that we determine which factors support development for different children in different academic placements and family settings. We cannot assume that simply because deaf children with cochlear implants are in mainstream classrooms, they learn just like hearing children. Nor can we assume that when children with implants are in separate classrooms, they learn just like other deaf children. Only when we fully understand how children with implants think and learn will we be able to adjust our instructional methods to match both their strengths and their needs.

Preparation of this report was supported by grants REC-0307602 and REC-0633928 from the National Science Foundation. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors are grateful for comments and suggestions provided by Sue Archbold, Gerard O'Donoghue, Irene Leigh (Action Editor for this manuscript), and three anonymous reviewers. No conflicts of interest were reported.

References

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Effects of Cochlear Implants on Children's Reading and Academic Achievement | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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This article presents a critical analysis of empirical studies assessing literacy and other domains of academic achievement among children with cochlear implants. A variety of recent studies have demonstrated benefits to hearing, language, and speech from implants, leading to assumptions that early implantation and longer periods of implant should be associated with higher reading and academic achievement.

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reading and spelling abilities

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Abstract

Attaining functional literacy is a key to success in a literate society, yet many children and young people, especially those who are born deaf or become so in the first months of life, find this a difficult if not impossible achievement. Numerous studies, conducted over the last 30 years, have shown that the great majority of deaf children find literacy a challenge, achieving significantly less than hearing peers in both reading ( Allen, 1986; Conrad, 1979; DiFrancesca, 1972; Lane & Baker, 1974; Lewis, 1996; Moog & Geers, 1985; Trybus & Karchmer, 1977) and writing ( Mayer, 1998, 2007).

The gap between deaf children and hearing peers tends to widen with age ( Marschark & Harris, 1996) and so difficulties become more apparent as children progress through school. A recent study ( Kyle & Harris, 2010) found a mean delay of 1 year in the reading scores of a group of 8-year-old deaf children: This had increased to a 3-year delay at age 11 years. A comparable cohort of 14-year-old deaf children ( Harris & Moreno, 2004) showed an average reading delay of over 4 years. These average delays hide a very considerable degree of individual variation. For example in, the Kyle and Harris cohort, some children made no reading progress at all in 1 year whereas others achieved a whole year of reading progress over the same period.

In order to understand why literacy is so challenging for many deaf children, it is pertinent to consider the skills that underpin learning to read an alphabetic script, such as English, for hearing children. Two key components-knowledge of spoken English and phonological awareness-have been found to underpin the development of literacy. Oral language and vocabulary predict reading development ( Bowey & Patel, 1988; Dickinson, McCabe, Anastasopoulos, Peisner-Feinberg, & Poe, 2003); and the ability to identify and manipulate phonemes within words-core aspects of phonological awareness-have been shown to be key to early reading success for hearing children ( Muter, Hulme, Snowling, & Stevenson, 2004).

The emerging picture of predictors of deaf children's literacy suggests that broadly similar skills are important although there are some notable differences. As with hearing children, the most compelling data come from longitudinal studies that have examined factors predicting reading outcomes over a period of 1 or more years. One such study ( Harris & Beech, 1998) found that, between the ages of 5 and 7 years, speech intelligibility, phonological awareness, and language comprehension predicted reading development. In a study of 6-year-old French children, early phonological awareness skills, including rhyme judgment and rhyme generation, predicted the reading progress made over 1 year ( Colin, Magnan, Ecalle, & Leybaert, 2007). A recent study ( Kyle & Harris, 2010) examined reading progress over a 3-year period, from the age of 7 years. Although most children showed reading delays at the end of the study, those with better vocabulary and speechreading skills at age 7 years exhibited less severe delays. This latter finding was consistent with the results of a comparison between matched groups of good and poor deaf readers at age 8 ( Harris & Moreno, 2006), which showed a significant difference in speechreading ability in favor of the better readers. All these studies, with the exception of Colin et al. (2007), assessed deaf children from a variety of educational settings so these findings are not specific to preferred mode of communication. Indeed, knowledge of spoken English and speechreading skills appear to be as important for children who sign and for those whose education is predominantly oral.

The longitudinal relationship between phonological awareness and speechreading appears to be a complex one. In Kyle and Harris (2006), phonological awareness was not correlated with reading ability at age 7 when the children were first assessed. However, at that age phonological awareness was strongly associated with speechreading. Early speechreading also predicted phonological awareness 1 year later. By the end of the study, 3 years later, phonological awareness was significantly correlated with reading ability ( Kyle & Harris, 2010) but it was reading ability that predicted later phonological awareness rather than the other way round. These patterns of results suggest that deaf children's phonological abilities may develop as a consequence of learning to read rather than being a prerequisite of reading, as in hearing children, and that, in the early stages, development is mediated by speechreading.

The complementary roles of speechreading-as a mediator of phonological awareness-and knowledge of English, especially English vocabulary, are illustrated by the finding from Kyle and Harris (2006) that speechreading was the strongest predictor of single word reading ability, whereas vocabulary knowledge was the strongest predictor of written sentence comprehension. This echoes the principle of the simple view of reading ( Hoover & Gough, 1990) that two components, decoding and linguistic comprehension, underpin the development of skilled reading. It would appear that this is equally true for deaf and hearing children.

Although speechreading appears to provide a route to the development of phonological awareness among deaf children, it might be expected that increased access to sound would promote knowledge of the oral language and provide a direct route to phonological awareness. This would, in turn, be expected to have an impact on literacy. The technological developments in hearing aids that have taken place over the last two decades have held out the prospect of increased access to speech for deaf children. Chief among the innovations that have taken place is the provision of cochlear implants. In the United Kingdom, these are provided by the National Health Service and so have become increasingly available for children, irrespective of parental means (Archbold, Harris, et al., 2008).

There is now a substantial body of evidence showing that cochlear implantation improves speech perception and production and facilitates the development of spoken language ( Archbold et al., 2000; Cleary, Pisoni, & Geers, 2001; Geers, 2002; O'Donoghue, Nikplopoulos, & Archbold, 2000; Pisoni & Geers, 1998; Tait, Nokolopoulos, Archbold, & O'Donoghue, 2001; Thoutenhoofd, et al., 2005; Watson, Archbold, & Nikolopolous, 2006; Watson, Hardie, Archbold, & Wheeler, 2008). In contrast to the findings about spoken language development, evidence concerning the impact on reading and writing has been inconsistent ( Archbold, Harris, et al., 2008; Marschark, Rhoten, & Fabich, 2007).

There are a number of issues to consider in evaluating literacy outcomes. Many children who receive a cochlear implant have complex needs that place limitations on their capacity to become literate ( Edwards, 2007). In addition, there are other deaf children who do not have any recognized additional needs but who have a level of nonverbal intelligence that is significantly below normal. Arguably, children with an IQ score that is more than 1 standard deviation ( SD) below the mean would not be expected to show age-appropriate levels of reading. However, many studies do not assess nonverbal IQ in evaluating literacy outcome following implantation so it is not possible to fully evaluate their findings.

Another important factor is age at implantation ( Archbold, Harris, et al., 2008). This has been falling steadily since the first pediatric implants were carried out and, in the United Kingdom, many children are now implanted around the age of 2 years or even younger. This was not the case a few years ago, and depending on the point at which data were collected, age at implantation may have been considerably higher than current norms. For example, a small-scale study ( Boothroyd & Boothroyd-Turner, 2002) found continued delays in reading ability 4 years after implantation but the average age at implantation was 5.8 years. Another small-scale study ( Spencer, 2004) investigated the language performance of children who had been implanted before 36 months. She reports reading comprehension scores within 1 SD of hearing peers and a strong correlation between spoken language and reading. In a study of 181 children, all of whom had received an implant before the age of 5 years ( Geers, 2003), over half were reading at an age-appropriate level. Notably, there was considerable variability within the group although a number of factors predicted reading competence, namely, mainstream educational placement, wide dynamic range using recent technology, longer memory span, and use of phonological coding. As in the Spencer (2004) study, reading ability was predicted by linguistic competence ( Tobey, Geers, Brenner, Altuna, & Gabbert, 2003). However Geers (2003) did not find an association between reading level and age at implantation as many other studies have done ( Archbold, Harris, et al., 2008).

Two recent European studies compared literacy attainment in deaf children with cochlear implants and peers with hearing aids. Among a sample of 152 deaf pupils in Scotland, those with cochlear implants scored comparatively higher on reading and writing than peers with hearing aids ( Thoutenhoofd, 2006); and a similar pattern emerged from a study of 550 deaf pupils in the Netherlands ( Vermeulen, van Bon, Schreuder, Knoors, & Snik, 2007). Notably, however, both studies found children with implants to be delayed when compared with hearing children. In the former study, the mean age at implantation was 37 months for primary school pupils and 91 months for secondary school pupils. In the latter study, the mean age at implantation was 74 months and the mean age at which reading was assessed was 153 months.

Perhaps the most optimistic view of the benefits of cochlear implantation comes from a study of children in the United Kingdom who were implanted by the Nottingham Cochlear Implant Team (Archbold, Harris, et al., 2008). This study followed up 105 children and assessed their reading levels at 5 and 7 years post-implantation. There was a wide variation in age at implant and so the sample was divided into those implanted relatively early (at or before the age of 42 months) and those implanted later, that is between 43 and 84 months. There was a strong and positive association between outcomes and age at implantation, and among the subgroup of children whose nonverbal IQ was 85 or above, those who had been implanted at or under the age of 42 months were reading at an age-appropriate level at both assessment points.

Seven years post-implantation most of the children in the Archbold, Harris, et al. (2008) study were approaching the end of primary schooling where the demands of literacy begin to increase as readers are required to deal with more complex sentences, abstract concepts, and the integration of ideas across extended text. There are analogous demands on writing skills. These demands continue to increase as children move into secondary school and, in their final years of schooling, adult levels of literacy are required. The question remains as to how well children and young people with a cochlear implant fare as they cope with these demands. Evidence from a follow-up study ( Geers, Tobey, Moog, & Brenner, 2008) suggests that early levels of attainment tend not to be sustained. Geers et al. (2008) were able to follow up 26 of the children assessed in an earlier study ( Geers, 2003) and found that, although they were reading at an age-appropriate level when they were 8-9 years old, they had an average reading delay of 2 years by the time they were 15-16 years old (for a discussion of this finding, see Marschark et al., 2007). This suggests that early reading success following a cochlear implant may not be sustained in the final years at school.

The Archbold, Harris, et al. (2008) study focused exclusively on children with a cochlear implant and it did not compare outcomes with those of deaf peers using a conventional hearing aid. There have been considerable technological advances in hearing aids ( Ackley & Decker, 2006) and many children in the United Kingdom are now using digital aids that would be expected to give them better access to speech. It is therefore legitimate to ask whether children with a cochlear implant have higher literacy levels than peers who rely on hearing aids.

The aim of this article was to examine the literacy attainment of deaf children aged between 12 and 16 years. The study compared three groups of children who were similar in age, nonverbal IQ, and levels of hearing loss but who differed in the type of aid that they were using. One group used hearing aids, and two had received a cochlear implant either before 42 months or later. The study assessed reading of both single words and text, using standardized tests, and also gathered data on spelling. The spelling data were designed to reveal spelling strategies. Previous studies ( Harris & Moreno, 2004, 2006; Mayer, 1998; Sutcliffe, Dowker, & Campbell, 1999) have shown that, in comparison to hearing children of similar reading age, deaf children tend to make very few phonetic spelling errors that correctly capture the sound of a spoken word. Such errors (e.g., spelling scissors as sissers) are very common in the misspellings of hearing children and are taken as evidence that they are using a phonological strategy. The study sought to find out whether there was evidence that children with a cochlear implant were making greater use of a phonological strategy than peers with hearing aids. Finally the study examined the relationship between use of phonological coding and reading level to see whether good readers made greater use of a phonological strategy than poor readers.

Methods

Participants

There were three groups of children and young people in this study, 27 deaf participants with digital hearing aids, 30 early implanted (at or before 42 months), and 29 late implanted (after 42 months). They ranged in age from 12 to 16 years. The three criteria for inclusion were that all deaf participants had an unaided hearing loss of at least 85 in the better ear, a nonverbal intelligence score of at least 85, and no additional visual or cognitive problems. After initial assessment, 25 potential participants were excluded from the study either because their IQ score fell below 85 or (occasionally) because detailed records showed that their hearing loss did not meet our criterion.

Schools were initially approached through e-mails and, where there was a willingness to participate, the head teacher gave written consent. In addition, written consent was obtained from all parents unless they had assigned this responsibility to the school. In all, a total of 17 sites took part in the study. These included special schools for the deaf, specialist units for deaf and hard-of-hearing children attached to mainstream schools, and mainstream schools in England and Northern Ireland. Table 1 shows the distribution of participants over the three types of educational setting. Participants also varied with respect to their preferred mode of communication. This was determined by asking how they would prefer to communicate with the experimenter. The distribution of participants by preferred mode of communication is shown in Table 2.

Tasks

Assessment of reading.

All participants were initially given a test of single word reading ability, taken from the British Ability Scales II ( Elliott, Smith, & McCulloch, 1996). They were allowed to sign the words or to say them out loud. The reading age on this test was then used to identify which of the four levels of the Edinburgh Reading Test was appropriate for each child ( Edinburgh, 2002). The Edinburgh Reading Test assesses a wide range of reading comprehension skills including understanding of vocabulary, syntax, and sequence of ideas as well as story comprehension. It is suitable for deaf children as there is no requirement to read aloud.

Spelling test.

The spelling test was adapted from Harris & Moreno (2004). Their test was designed for use with deaf children, and each word to be spelled was depicted by a line drawing ( Snodgrass & Vanderwart, 1980). The original test was designed for use with younger children than those in this article and so some highly frequent monosyllabic words (e.g., car and fox) were replaced with less frequent multisyllabic words (e.g., kangaroo and magician). As in the original version, this test was not intended to produce a standardized spelling age but, rather, to provoke spelling errors. Thus, the majority of words were irregular (see Appendix 1).

The analysis of spelling errors was identical to Harris & Moreno (2004) and was designed to uncover the use of a phonological strategy. The primary analysis was concerned with phonetic errors. An incorrect spelling was classed as phonetic if it contained at least one instance where a sound was correctly represented by the use of an incorrect letter or letters. For example, the following spellings of PIGEON were considered to be phonetic: pigin, pidgeon, pigion, piggen, pigen, pegain, and pigion. Thus, in the case of PIGIN, the IN spelling is a legal, though incorrect, spelling of the second syllable, and in PIDGEON, PIDGE is a plausible spelling of the first syllable.

Nonverbal intelligence.

Nonverbal IQ was assessed using a subtest-Pattern Construction-from the British Ability Scale II ( Elliott et al., 1996). This has been used in previous research with deaf children and has been shown to correlate highly with the composite nonverbal IQ score that is measured using this subtest and two others. For example, in the data collected by Harris and Moreno (2004), the correlation of the matrices score with the composite score was over .7 ( r = .75, N = 62, p < .001). The main purpose of the IQ measure was to ensure that none of the participants had a mild or moderate learning difficulty that had gone undetected and so to ensure that the three groups were comparable in intellectual functioning ( Archbold, Harris, et al., 2008).

Procedure

All participants were assessed in their school. The Pattern Construction test, Single Word Reading Test, and spelling task were administered individually. The Edinburgh Reading Test was administered either individually or in small groups. All assessments were normally carried out in one session but, in some cases, it was necessary to complete assessments on a second day. In two schools, pupils had completed the Edinburgh Reading Test within 6 months and so they were not retested.

Results

There was a significant association between the type of aid participants used and their educational placement (χ ² = 16.88, degrees of freedom [ df] = 4, p = .002). Table 1 shows that all but two of the children and young people with hearing aids were in schools for the deaf whereas those with a cochlear implant were more equally divided between the three educational settings. There was also a significant association between aiding group and preferred mode of communication (χ ² = 12.16, df = 4, p = .016). As Table 2 shows, participants with hearing aids were almost equally likely to prefer sign, speech, or both whereas those with a cochlear implant preferred to use either speech or speech and sign together, but not sign alone.

Table 3 shows the mean chronological age, hearing loss, age at diagnosis, age at implantation, and nonverbal IQ for the three groups. Participants were well matched for chronological age and nonverbal IQ. There was a small, but significant, difference in degree of hearing loss in favor of participants with hearing aids. Post hoc analysis revealed a significant difference between the hearing aid and late-implanted groups ( p = .015), but no other comparisons were significant. There was also a significant difference in age of diagnosis. This was highest in the late-implanted group who were, on average, not diagnosed for hearing loss until 16 months of age. Post hoc analysis revealed a significant difference between the late- and early-implanted groups ( p = .016), but no other comparisons were significant. In light of these intergroup differences, regression analyses were carried out to determine whether either of these variables was a significant predictor of reading scores. These showed that neither single-word reading nor the Edinburgh scores were predicted by degree of hearing loss or age of diagnosis.

As might be expected, the three literacy measures were highly intercorrelated. Table 4 shows the correlations between the two reading measures and the spelling score, partialling out both chronological age and nonverbal IQ. The highest correlation of just over .7 was between the two reading measures with smaller but significant correlations of just over .5 between each of the reading measures and the spelling score.

Table 5 shows the mean reading and spelling scores for the three groups. It can be seen that all groups showed reading delays on both reading tests but there was very considerable variability in performance within each of the three groups, as indicated by the wide range and large SD. In all groups, some participants were reading very well-above their chronological age-whereas others were reading several years below. In this respect, these findings appear very similar to those of earlier studies of literacy in comparable UK populations ( Harris & Moreno, 2004, 2006; Kyle & Harris, 2006, 2010).

For single-word reading, there was relatively little difference between the three groups, and this was confirmed by analysis of variance (ANOVA) ( F[2, 83] = 0.13, not significant [NS]). There was a larger difference for the Edinburgh Reading Test with the smallest delay-just over 2 years-being shown by participants with hearing aids. ANOVA confirmed that the effect of aiding group was just significant ( F[2, 83] = 3.11, p = .05, η ² = .07). Post hoc testing, using Tukey, indicated that there was a significant difference between the hearing aid and early-implanted groups (mean difference = -22.55, p = .049), but no other contrasts were significant. There were no significant differences among spelling scores ( F[2, 83] = 2.41, NS) although, as for the Edinburgh Reading Test, it was the pupils with hearing aids who performed best.

In order to gain a clearer picture of reading levels in the three groups, participants were divided into two subgroups. Following earlier comparisons (e.g., Harris & Moreno, 2006), those reading within 12 months of chronological age on the Edinburgh Reading Test were classed as good readers. Those reading at 12 months or more below chronological age were classed as poor readers. Using this classification, 13/27 participants with hearing aids were classed as good readers. This compared with only 6/30 in the late-implanted group and 5/29 in the early-implanted group. Chi square revealed a significant association between aiding and reading level (χ ² = 8.07, df = 2, p = .018), and further analysis, combining the two groups with a cochlear implant and using Fisher's exact test, showed a highly significant association between the type of hearing aid and reading level ( p = .009).

Because there was an association between type of hearing aid and educational setting, a further analysis was carried out to examine the association between setting and reading level. Thirteen participants were in a mainstream school and almost half (46%) were good readers. Eighteen were in a specialist unit but only one was a good reader (6%). In all, 55 of our participants attended a school for the deaf and 17 (i.e., 31%) were good readers. Chi square revealed a significant association between educational setting and reading level (χ ² = 6.87, df = 2, p = .032).

The final analysis was concerned with the prevalence of phonetic spelling errors. As can be seen in Table 5, the overall mean score for correct spellings was 27 out of 40 and the SD was almost 10. This reflects the fact that the best spellers were at or close to ceiling on the spelling test. In all, 14 participants achieved scores of 38 or more. In order to examine phonetic errors, these good spellers were excluded. For the remaining 72 participants, the relationship between percentage of phonetic errors and reading and spelling was examined. There was a significant correlation between percentage of phonetic errors and single-word reading ( r = .34, N = 72, p = .004) and a smaller, but still significant, correlation with score on the Edinburgh Reading Test ( r = .24, N = 72, p = .047). There was also a significant correlation between percentage of phonetic errors and number of correct spellings ( r = .31, N = 72, p = .009). There was, however, no difference among the groups in the percentage of phonetic errors ( F[2, 69] = 0.21, NS).

Discussion

The main conclusion to emerge from this study is that, between the ages of 12 and 16 years, children and young people who had received a cochlear implant were reading no better than their peers who were using a hearing aid. Furthermore, on the Edinburgh Reading Test, the highest proportion of participants reading within 12 months of chronological age was in the group with hearing aids; and, overall, those in the hearing aid group read significantly better than those with a cochlear implant, inserted before 42 months.

Before reflecting on these findings, and their implications, it is pertinent to consider whether there were any systematic differences among the participants in the three groups that might have had an impact on their literacy skills. The most likely participant characteristics to affect literacy are chronological age, nonverbal IQ, age of diagnosis, and degree of hearing loss. Participants in this study were well matched for both chronological age and nonverbal IQ. There were small, but significant difference, in both unaided hearing loss and age of diagnosis but a regression analysis showed that neither was a significant predictor of reading level. Only one participant in the sample became deaf as a result of meningitis at the age of 24 months. In all other cases, it is likely that the hearing loss was present from birth although it was not always diagnosed until a few months later because Universal Newborn Nearing Screening was only at a pilot stage in the United Kingdom at the time these children were born. On these measures, then, there do not appear to be any systematic differences among participants in the three groups that might account for differences in reading level.

If we accept the conclusion that pupils with a cochlear implant were not showing age-appropriate levels of literacy, this can be seen as consistent with the finding of Geers et al. (2008) that early cochlear implantation does not result in age-appropriate reading levels at age 15-16 years for the majority of students. At first sight, the present findings are not, however, consistent with those of Archbold, Harris, et al. (2008) that children with an early cochlear implant were reading at an age-appropriate level. The children and young people in this article were similar in terms of nonverbal IQ and degree of hearing loss to those assessed by Archbold, Harris, et al.; and the division of age at implantation into early and late was made at an identical age point of 42 months. The children in both studies also came from the United Kingdom and the exclusion criteria were identical. Furthermore, both studies used the Edinburgh Reading Test to assess reading. There was, however, one important difference.

In the study of Archbold, Harris, et al., the children with an early implant were aged between 16 and 41 months when they received their implant. This means that they were between 8 and 10 years old at the time of the assessment that was carried out 7 years after implantation. They were therefore several years younger than the children and young people in this article whose mean age was nearly 14 years. Because Geers et al. (2008) found that there was a decline in reading ability between the ages of 8-9 and 15-16 years, the relatively poor performance of the pupils in this article may be explained by the fact they were older. There is some evidence of a fall off in reading ability between the two post-implant assessment points in the Archbold, Harris, et al. (2008) study. Not all of the children were seen at the later assessment point and so some caution has to exercised in making a direct comparison between the two points. However, at 5 years post-implant, the children with an early implant were reading 8 months above chronological age but 2 years later they were reading just below chronological age level.

Marschark et al. (2007) suggest that the fall off in reading progress for children with cochlear implants may arise because the latter stages of acquiring literacy are not supported by explicit teaching. However, this is the case for all children and young people learning to read and so the issue is why this might pose a particular problem for deaf people. In a seminal paper, Stanovich famously coined the term "Matthew effect" to characterize these later stages of learning to read where it is the process of reading itself that drives further progress: Children who read a lot become increasingly competent readers ( Stanovich, 1986). One particular aspect of reading competence that plays an important role in higher level literacy is the ability to read infrequent and abstract vocabulary. Unlike early vocabulary development, which is driven by exposure to spoken (or signed) language, later-acquired vocabulary comes mainly through exposure to complex text. Consider as an example, some of the items in the British Ability Scales word reading test that we used in this study. At the age of 14 years, children are expected to be able to read word like GENERATION and CHARACTER. By the time they are 17, they are expected to read CATASTROPHE and METICULOUS and to attain a reading age of 18 years they need to know words like MNEMONIC and ARCHAIC. Words like these are going to be encountered most often when reading rather than in everyday conversation.

Exposure to print interacts with reading ability. Many studies, including those carried out by Stanovich and colleagues, have shown that early reading levels are a good predictor of later levels. For example, in a longitudinal study that followed a group of children from 1st to 11th grade (Cunningham & Stanovich, 1997), reading in first grade was a strong predictor of reading 10 years later even after individual differences in cognitive ability had been accounted for. However, a key factor in the sustained development of reading ability was continuing exposure to print. As predicted by the 'Matthew effect', children who read a lot continued to develop their reading skills. Cunningham and Stanovich suggest that children who find it easy to learn to read will most naturally spend time reading. Conversely, children who find reading difficult will be less inclined to do so. So the question arises as to how much time deaf children and young people devote to reading?

This is clearly an important issue for future research but there are reasons for thinking that many deaf readers will be less inclined to read for pleasure than hearing peers. In this article, there was an association between the use of phonological coding - as evidenced by the presence of phonetic spelling errors in - and reading ability. As might be expected, the correlation was highest for single-word reading where the effect size was medium. This finding is consistent with that of Geers (2003) that phonological awareness was a predictor of reading level among children with cochlear implants. Because phonological skills continue to be important both for the acquisition of new vocabulary in reading and for reading itself, weak phonological skills could be seen as a barrier to becoming a skilled reader. In other words, poor phonological skills - and hence poor decoding skills - make reading a difficult task and so do not pave the way for reading fluency to develop.

The finding that many adolescents with a cochlear implant are not reading at an age-appropriate level can be seen as consistent with previous findings. However, the suggestion that peers with hearing aids may actually be doing better will be seen as controversial. What might explain this finding in this article? After all, the general point that reading becomes an increasingly self-driven process as children progress through school applies equally to children with a cochlear implant and to hearing aid users. There is, however, one difference between the two groups that might be important. As shown in Table 1, none of the children with hearing aids was in a mainstream school and the majority attended a school for the deaf. By contrast, 13 of the children with cochlear implants were attending a mainstream school and a further 16 were in a specialist unit where they would spend some time in a mainstream classroom. Might it be the case that the supportive environment of a school for the deaf provides a better setting for the continuing development of literacy skills?

This is a difficult question to answer, not least because the placement of deaf pupils in a particular educational setting is determined by many different factors including local policy, pupil and parental preferences, and the perceived need for additional support. It is important to note that almost half the participants in our study were reading very well in a mainstream classroom. The setting that appeared most problematic was the specialist unit attached to a mainstream school. Placement in such a unit, rather than solely in a mainstream classroom, implies a perceived need for continuing support and it may be for these children, for whom acquiring literacy is a struggle, that the support of a school for the deaf is particularly beneficial.

There is some evidence that children placed in a mainstream setting can experience difficulties. A study of children with cochlear implants, who were aged between 7 and 12 years and in mainstream schools ( Mukari, Ling, & Chani, 2007), noted their "inability to access classroom instructions delivered orally." Others have emphasized the crucial role of education in ensuring the children with cochlear implants develop to their maximum potential, highlighting the need for continuing specialist teaching even when children are taught in a mainstream classroom ( Archbold, O'Neil, & Gregory, 2008). What is clear from our study is that, even with the considerable advances in hearing aid technology, many deaf adolescents continue to find literacy challenging, and it appears that many will require specialist support throughout their time at school.

Funding

Economic and Social Research Council (RES-000-22-2947) to M.H.

Conflicts of Interest

No conflicts of interest were reported.

We would like to thank all the pupils and teachers who assisted us in our study.

Appendix

References

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Reading and Spelling Abilities of Deaf Adolescents With Cochlear Implants and Hearing Aids | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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Attaining functional literacy is a key to success in a literate society, yet many children and young people, especially those who are born deaf or become so in the first months of life, find this a difficult if not impossible achievement.

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CI and complex needs

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Abstract

This article will focus on a special group of children with cochlear implants: those who have difficulties or disabilities in addition to their deafness which make the task of meeting their complex needs particularly challenging. A figure of 30%-40% of deaf children is consistently quoted as being the proportion with additional disabilities, although definitions of what constitutes an additional disability do vary. Gallaudet University (Gentile & McCarthy, 1973) defines them as "any physical, mental, emotional or behavioral disorder that significantly adds to the complexity of educating a hearing-impaired child". The World Health Organization (1980) also provides a useful conceptualization in this context, proposing three levels: (a) impairment-any loss or abnormality of psychological or anatomical structure, (b) disability-the restriction or lack of ability to perform an activity considered normal for a human being, and (c) handicap-the disadvantages for an individual resulting from an impairment or disability that limits or prevents fulfillment of a role normal for that individual. Some deaf individuals would argue that deafness by itself is not an impairment and therefore does not result in either disability or handicap, as defined by the WHO. However, it is less contentious to consider many of the additional difficulties or impairments sometimes associated or coexisting with hearing impairment, as causing both disability and handicap. Such impairments include cognitive or learning difficulties, global developmental delay, visual impairment, language and communication disorders (autistic spectrum disorders [ASDs]), and other medical, physical, or motor problems. In addition, some deaf children may exhibit difficulties that are more psychological in nature, for example, behavioral or emotional disorders, which equally may prevent normal development and functioning. However, cognitive ability is a continuum, and all the other types of impairments mentioned above range in severity, so it is not always clear-cut when a deaf child should be considered to have "complex needs."

In the early years of pediatric cochlear implantation, it was typical for children with known, significant additional disabilities to be considered unsuitable for the procedure. However, the criteria for candidacy have broadened in many respects in recent years, such that children with multiple handicaps and complex needs are now assessed, and many go on to receive implants. The aims of this paper are to review the literature on outcomes following cochlear implantation in children with complex needs and then to consider the challenges facing pediatric cochlear implant teams when working with this group of children, from psychological perspectives in particular.

Review of Outcome Research

There is now a very considerable body of evidence regarding the outcomes of cochlear implantation for deaf children in terms of speech perception, speech intelligibility, and communication and language development. To a much lesser degree, but slowly increasing, are studies reporting on the educational, psychological, and social consequences of implantation for the child and family (for a comprehensive review of studies published between 1994 and 2001, see Thoutenhoofd et al., 2005). However, the majority of these studies are of little relevance to those seeking information on the benefits (or otherwise) of cochlear implants for deaf children with complex needs. The reason for this is twofold. Firstly, as mentioned previously, children with additional difficulties were typically precluded from receiving an implant in the early years of the use of implants, and therefore historical data from such cases do not exist. Secondly, in more recent years, although many such children have been implanted, the vast majority of published empirical studies specifically exclude them from their samples, in order to try and make their groups as homogeneous as possible. As a result, there are only a very small number of papers that specifically focus on outcomes in children with additional disabilities.

Cognitive/Learning Disabilities

In one of the earliest papers to be published on this topic, Pyman, Blamey, Lacy, Clark, and Dowell (2000) examined speech perception outcomes in 75 children aged up to 5 years, comparing the results of children with and without cognitive and/or motor delays. They found that the former group was significantly slower in developing speech perception skills following implantation. Interestingly, there was no significant association between outcome and the etiology of deafness per se. Thus, they speculate that the wide variety of outcomes among children with the same syndrome or etiology of deafness are individual variations in the functioning of higher centers of the brain.

Fukuda et al. (2003) provided a case study on a 10-year-old congenitally deaf child described as having moderate mental retardation, who received a cochlear implant at 4 years of age. Before implantation, his language development was delayed by 34 months in comparison with his chronological age. This gap had narrowed to a 23-month delay, 2 years after surgery, but the extent of his cognitive delay remained unchanged at around 15 months delay. The child was able to use intelligible three-word sentences within 2 years of implantation. They recommended that multiply handicapped children be considered for cochlear implantation under certain preoperative conditions, including that the child responds to signed communication and produces at least two-word sentences (in sign).

Recently, Holt and Kirk (2005) reported the results of a retrospective study examining the speech and language outcomes of children with cochlear implants who have a cognitive delay, defined as scoring more than one standard deviation below the mean on tests of cognitive function and/or identified as developmentally delayed by a psychologist. Children with any additional disabilities other than cognitive delay were excluded. They compared 19 children with cognitive delays (average estimate of cognitive functioning in the borderline to mildly impaired range) with 50 children with no additional disabilities, up to 2 years after implantation. Both groups demonstrated significant improvements in speech and language skills over time, but the children with cognitive delays had significantly lower scores on two of the three measures of receptive and expressive language and slower rates of auditory sentence recognition development compared with their normally developing implanted peers. Children with cognitive delays made relatively good progress in developing speech perception skills but struggled with the tasks involving higher level language skills such as sentence recognition and receptive and expressive language. They concluded that cochlear implantation in children with a mild cognitive delay does produce sufficient benefit to make it an appropriate intervention for this group, but also question how benefit should be defined, and highlight the need for appropriate assessment measures for children with additional disabilities.

Although these three studies (and others on implanted children without complex needs, e.g., Moog & Geers, 2003) do suggest a strong association between cognitive ability and outcomes, as yet it is not possible to confidently define the level of ability below which a cochlear implant may be considered nonbeneficial.

Global Developmental Delay

Generalized developmental delay frequently also entails some degree of cognitive or learning delay, but in young children, particularly those with limited language skills, it is difficult to accurately determine the level of cognitive functioning using traditional standardized assessments. Consequently, a global index of development is a more suitable measure of additional difficulties in young implant candidates. To date, one published study has explicitly examined outcomes following cochlear implantation in relation to developmental status before implant. Edwards, Frost, and Witham (2006) found that children implanted before the age of 3.5 years who had a mild developmental delay did make gains in speech perception skills and in speech intelligibility over a 2-year period, but at a slower rate than their normally developing implanted peers. However, a small group of children whose general development was significantly delayed (by around 12 months or more) made almost no progress in terms of speech perception or speech intelligibility.

Visual Impairment

Saeed, Ramsden, and Axon (1998) described the progress of two visually impaired children who received cochlear implants, one of whom was congenitally deaf-blind due to rubella infection. The authors described modest gains in identification of speech sounds and the ability to follow simple auditory instructions 2-3 years after implantation. El-Kashlan, Boerst, and Telian (2001) also reported positive results in two pediatric cases where deafness was accompanied by significant visual impairment. The first child, who had Usher's Syndrome Type 1, was implanted at 3.5 years and started losing her sight at 7 years of age. She developed excellent open-set speech recognition skills and attended a mainstream school. The second child, a little boy with multiple medical problems and delayed general development, had only had his implant for 6 months at the time the authors made the report, so gains were minimal at that time.

Autistic Spectrum Disorders

ASDs remain one category of additional difficulties where great caution is exercised by cochlear implant teams when considering a child for an implant. Hearing children with autism generally have major difficulties communicating effectively and in developing spoken language skills and may have sensory integration difficulties. It is therefore not surprising that deaf autistic children are not usually considered good candidates for cochlear implantation. Thus, in the past, a confirmed diagnosis of autistic spectrum disorder has typically been given as a contraindication to implantation. However, with the steady decrease in age at which children are being assessed and implanted, the number of children in whom autism is only diagnosed after they have received an implant is slowly increasing. Also, as the term ASD implies, there is a range of level of disabilities in autistic children, with some relatively minor social communication difficulties and little or no general cognitive impairment, through to severe language, cognitive, and behavioral difficulties. Therefore, careful assessment of the extent of impact of ASD in each individual case is essential in deciding whether a cochlear implant may be an appropriate intervention. Having said all this, at present there is only one paper that specifically reviews the progress of children with ASD who have cochlear implants. Donaldson, Heavner, and Zwolan (2004) described the progress of seven children, aged between 3 and 9 years at implantation, who had been using their implants for between 6 months and 5 years. Four children were diagnosed with autism and two with pervasive developmental disorder, with a range of severity of presentations and disabilities. Many of the children were unable to complete some of the standard speech and language measures due to their developmental level. However, where data were available, they indicated modest gains, but these were small compared with a normal implant population. In addition, these authors used a survey to explore parents' perceptions of the impact of the implant on their child's functioning. This revealed benefits in terms of improved eye contact, awareness of the environment, reaction to music, vocalization, use of sign language, and response to requests. Overall, most families said they would recommend an implant to other families in a similar situation. The authors stress the importance of counseling parents about the potential benefits of an implant, emphasizing that the implant is likely to have little or no impact on the diagnosis of ASD and that oral communication is unlikely to be a realistic goal.

Two other studies have included children diagnosed with ASD in their sample. However, because these studies did not focus specifically on these children, they will be described in the next section.

Mixed Additional Disabilities

In one of the largest studies of implanted children with multiple handicaps, Waltzman, Scalchunes, and Cohen (2000) documented the progress of 29 children with disabilities ranging from attention-deficit disorder, dyspraxia, or central auditory processing disorder to autism, learning disability, or cerebral palsy. Many of the children were unable to perform the standardized tests either preoperatively or at many of the time intervals after implant. However, some children did gradually become able to attempt the tests and go on to slowly develop the ability to perceive phonemes, words, and sentences using audition alone. Just over half of the children used oral communication, rather than total communication or sign. They concluded that "multiply handicapped children obtain demonstrable benefit from cochlear implantation," but the rate of development of auditory perceptual skills is slower than for other deaf children with implants. Although these results are encouraging, unfortunately they tell us little about whether the children have been able to use their improved perceptual abilities to also develop their communication and language skills.

In a smaller study of 10 multiply handicapped children, Hamzavi et al. (2000) used the Evaluation of Auditory Responses to Speech (EARS) battery to assess progress following implantation. Again a range of disabilities are represented, including severe learning difficulties, blindness, hyperactivity, psychomotor retardation, and autism. Similarly they report a wide range of outcomes, from no speech recognition or production through differentiating voices and using some vocalizations to communicate, through to the ability to use simple sentences. These authors also concluded that multiply handicapped children (and their parents) do benefit from cochlear implantation despite not being considered traditionally "good" candidates.

Vlahovic and Sindija (2004) described an even smaller sample, of four children with additional disabilities including communication disorder, moderate psychomotor retardation, and attention-deficit hyperactivity disorder, following implantation. They used the Categories of Auditory Performance and Speech Intelligibility Rating scales, the Listening Progress Profile, and part of the EARS test to monitor progress. They reported better than anticipated perception skills but less satisfactory speech development (speech was mainly unintelligible).

Filipo, Bosco, Mancini, and Ballantyne (2004) assessed outcome from a different perspective in their study that included 18 deaf children with additional disabilities or associated problems (bilingualism or family problems in 10 of the cases). They focused on psychosocial as well as audiological and/or communication outcomes, examining the child's self-help skills along with social and family relationships using a mixture of observation, questionnaires, analysis of drawings, and structured interviews. They reported gains in listening, communication, and self-sufficiency while family and social relationships remained stable and concluded that in such special cases cochlear implantation is a positive intervention.

Finally, Wiley, Jahnke, Meinzen-Derr, and Choo (2005) also moved away from the usual methodology for assessing outcome of pediatric cochlear implantation, to examine the qualitative benefits for children with additional disabilities using a mixture of open- and closed-ended questions that were then coded by themes. Fifteen families were recruited whose child had had at least 6 months of cochlear implant use with additional disabilities including visual impairment, mild motor disabilities, cognitive disabilities (nonverbal IQ of 75 or lower), specific learning disability, and language or behavioral disorders. Some of the disabilities were diagnosed after implantation. All the families felt that their child had made progress in developing communication skills and was more attentive and interested in the world around them, and all parents were happy with their decision to have their child implanted. However, the authors recognized that the qualitative methodology used may have led to bias in reporting of skills and inconsistencies in interpreting communication behaviors.

Specific Syndromes/Etiologies of Deafness

In contrast to the studies described above where samples have been heterogeneous in terms of the etiology of deafness and additional disabilities, in this section the papers to be considered have focused on groups of children with the same cause of deafness.

Two studies have reported on the outcomes following cochlear implantation in children with CHARGE association. Bauer, Wippold, Goldin, and Lusk (2002) described variable degrees of objective benefit from the procedure for five children. In a single case study report, Au, Hui, Tsang, and Wei (2004) described the progress of a boy with CHARGE association who received a cochlear implant at the age of 2.5 years. At 7 years of age, after "intensive rehabilitative training," his receptive and expressive language age equivalents were 2.05 and 2.04 years, respectively. Speech perception performance did improve, although even at 4 years after implant he was unable to correctly identify the Ling sounds, number of syllables, vowels, or consonants with 100% accuracy.

Both Lee, Lustig, Sampson, Chinnici, and Niparko (2005) and Ramirez Inscoe and Nikolopoulos (2004) noted that children whose cause of deafness is human cytomegalovirus (CMV) contracted perinatally are at risk of experiencing developmental neurological deficits, including learning difficulties. Ramirez Inscoe and Nikolopoulos compared 16 children deafened by CMV with 131 children with all other etiologies and found a wide range of speech perception and speech intelligibility outcomes, which were, on average, poorer in the former group. Lee et al. (2005) also found improvements in speech perception in 13 children with CMV-related deafness and describe these as being within the range established by their overall pediatric implant population.

The last group of children to be considered here is that of children with hearing impairment secondary to meningitis. In some ways, they constitute a very different group from the others as these children will have experienced sound prior to the meningitis and, depending on their age, may have well-developed oral language skills. However, as a result of the infection, this group is at high risk of having additional difficulties, particularly in the cognitive domain. Isaacson, Hasenstab, Wohl, and Williams (1996) reported that of the 10 pediatric cochlear implant patients in their sample, five had documented learning disability. This group showed slower progress in developing auditory perception and receptive language skills, as well as sequential organization abilities, and lower overall test scores, compared with postmeningitic children with no learning difficulties. More recently, Francis et al. (2004) compared 30 postmeningitic children with children deafened by other causes, matched for age at diagnosis, age at implantation, age of first hearing aid use, and method of communication at home or school. Cognitive abilities were assessed before implant, and speech perception skills were measured within the first 2 years following implantation. They found no difference between the groups on overall cognitive or speech perception performance, but the authors did note that where there had been postmeningitic hydrocephalus, the gains in speech perception were significantly smaller.

Comment

Given the small number of studies available, the typically small numbers of cases in those studies and the wide variety of types and severity of additional disabilities, drawing conclusions about the outcome of cochlear implantation in children with additional disabilities is a challenge. The confounding variables are particularly difficult to account for when considering specific etiological groups, when the number of cases becomes even smaller, or when single cases are reported. Also, even in these defined groups, the range and severity of additional disabilities may be great. Thus, the generalizability of the findings is probably low. Methodologically, the studies are also heterogeneous. Those that follow a specific group longitudinally provide interesting information about the development of certain skills, usually in the first few years following implantation. Most of these focus primarily on speech perception skills or speech intelligibility, which, although important, do not tell us much about the children's communication skills in the "real world" or their language abilities. Is the progress of these children meaningful, or simply measurable? Studies that have employed a comparative design give a clearer indication of the gains made by the children with complex needs relative to the general pediatric implant population. However, these are few and far between. More generally in terms of methodology and evidence-based practice, pediatric cochlear implantation is not a procedure that lends itself to conducting randomized, controlled, double-blind trials for a variety of ethical and practical reasons. Cohort studies, with or without an appropriate control group, are rarely large enough to allow sufficient power for the results to be interpreted confidently.

To date, there are no published studies (let alone ones which are methodologically robust) that specifically explore the progress of children whose additional difficulties have only been identified after they received their cochlear implant. Such children would include those who were implanted at a very young age or have autistic spectrum disorders or other communication or language disorders that could not be diagnosed before implantation. Other groups could include those diagnosed with dyslexia or dyspraxia, which often occurs many years after receiving their implant. In terms of outcome research, there is a danger of becoming tautological here as the lack of progress in developing language and literacy skills is often the trigger for these children being assessed and diagnosed, and therefore, by definition they will show poorer outcomes compared with the general cochlear implant population. Research by David Pisoni and his colleagues since the late 1990s has sought to identify what factors distinguish "star" implant performers from those who struggle to develop oral language skills, focusing on cognitive functions such as working memory. This is a particularly promising area of research, which has the potential to both increase our understanding of how children learn to decipher the electrical stimulation provided by a cochlear implant and also to devise remedial activities for those children who are having difficulty making sense of the auditory input.

Despite the methodological issues, a few tentative conclusions may be drawn and a number of important themes have arisen from the work described above. Overall it appears that where there is a cognitive or global developmental delay, when this is mild, the children can make measurable progress, and outcomes may be within the same range as those of the pediatric implant population overall. However, where there are significant learning disabilities or a severe global developmental delay, outcomes are much less favorable; although children may develop some speech perception skills, there is less evidence that they progress to using this information to improve their oral communication and language skills. It also appears that it is probably the severity of any cognitive deficits that is predictive of outcome rather than etiological factors per se.

Almost all the authors emphasize the importance of a comprehensive, multidisciplinary preimplant assessment and the need to consider the appropriateness of cochlear implantation for each child on a case-by-case basis. Many authors also stress the need to broaden the nature of outcome measures to include indices of subjective benefits such as improved quality of life and family relationships. Finally, preimplant counseling for the parents of pediatric implant candidates regarding likely benefit and outcomes is strongly advised for children with additional disabilities by many authors. However, it has to be acknowledged to parents that although there are some factors that are known to be associated with better outcomes, it is not possible to precisely predict the degree of benefit based on the empirical evidence available to date.

Challenges for Clinical Practice

The following sections will consider the practical implications and challenges of implanting children with additional disabilities, particularly from a psychological perspective.

Children Whose Additional Disabilities Are Identified Before Implant

There are two main groups of children who come under this category-those whose complex needs have been well documented in their medical histories prior to being referred for cochlear implant assessment and those whose additional difficulties are identified as part of the cochlear implant assessment process. This latter group is not as rare as one might expect as many difficulties may be relatively subtle in nature, particularly in young children, yet potentially have a profound negative impact on the benefit to be gained from a cochlear implant. For example, emerging autistic spectrum disorder, language disorders, or attention-deficit/hyperactivity disorder may only be formally recognized when the child is undergoing the multidisciplinary assessment of hearing, speech and language skills, cognitive abilities, behavior, and general development. Up until that point, it may be that the child has not been put in a situation where such difficulties are likely to be apparent, and subtle additional difficulties are frequently attributed to the child's deafness and lack of understanding of communication and language, rather than being considered additional difficulties in their own right. In many children, the etiology of deafness is known and additional disabilities are common, as for example in CHARGE association, CMV infection, significant prematurity, rubella, meningitis, and toxoplasmosis infection. However, the range and severity of the disabilities are wide, and the assessment for cochlear implantation can be useful in identifying and describing these difficulties more fully. In particular, the psychological assessment may clarify the extent of any general developmental delay or identify specific cognitive deficits that have implications for rehabilitation, outcome with the implant, and development generally.

Thus, one of the major challenges for the psychologist on the implant team is to tailor their assessment to the specific presentation and needs of the child. For the majority of pediatric implant candidates, it is possible to follow an assessment protocol, which is likely to be broadly similar between implant programs. The areas typically explored would include the child's general development, nonverbal cognitive abilities (if old enough to complete a standardized test), emotional and behavioral factors, learning style, communicative intent, social skills, play skills, and family expectations and support. In older children, the assessment would also explore issues around academic achievement, peer relationships, and self-concept. A central part of the protocol is the assessment of nonverbal cognitive abilities using standardized measures appropriate for hearing-impaired children, such as the Leiter International Performance Scale-Revised (Roid & Miller, 1997) or the Snijders-Oomen Non Verbal Test Revised ( Snijders, Tellegen, & Laros, 1989). For very young children, that is, those under 2 years of age, a developmental test such as the Griffiths Scales of Mental Development ( Griffiths, 1984) or the Schedule of Growing Skills II ( Bellman, Lingham, & Aukett 1996) may be used to assess functioning in domains not related to hearing, speech, or language (as these will be covered in depth by the implant team Speech and Language Therapist). The main problem with all these tests when considering a child with complex needs is their reliance on adequate vision and fine motor skills for their completion. Although the manual for the Leiter R states that it is acceptable for the testee to respond using eye pointing if necessary, in practice this is an extremely difficult response to interpret reliably and is a method of communication encountered only very rarely. Therefore, gaining an accurate impression of the child's cognitive abilities and learning capacity may not be possible, in those very children where this information may be central to deciding whether a cochlear implant is an appropriate intervention. Although it is important to identify significant learning difficulties where they do exist, it is equally, if not more, important to demonstrate the child's normal cognitive capacity where this may be obscured by overwhelming physical difficulties, as in severe cerebral palsy.

As a result of the problems administering standardized tests, it becomes necessary for the psychologist to think creatively about how to assess the child's learning capacity. One way to do this, which has proved useful on a number of occasions in the author's experience, is to devise one or more learning "tasks" for the child to tackle during the assessment period. The task should be set at an appropriate level of difficulty such that it is just beyond the child's current level of achievement, based on careful observation of the child's current skills and information from parents and other professionals involved, along with clinical judgment. Many young deaf children with additional disabilities have not been explicitly taught early skills such as matching (color, shape) or sorting (by size, color, shape), often as a result of emphasis on self-help or daily living skills. Parents typically require explicit instructions on how to teach their child these sorts of cognitive skills, including what materials to use, how to get their child to indicate choices, and how to monitor progress. At another level of cognitive functioning, it is very informative to focus on the child's development of symbolic understanding. Parents may be asked to keep a note of any example of when their child has indicated that he/she realizes that a picture of an object represents a real thing (e.g., the child may be looking at a picture of a cat in a book and then points to the family pet cat). If the parents are unable to identify any example of symbolic understanding from the child's everyday behaviors, it may be necessary for the psychologist to devise an appropriate task or activity to see if the child is able to develop this skill. At a more sophisticated level, it is very helpful to be able to show that the child is able to learn that signs such as those used in BSL are symbols representing real objects, even if the signs have to be adapted in some way to accommodate the child's visual or motor disabilities. If this level of learning and understanding can be reliably demonstrated, it might indicate that the child has the learning capacity to associate spoken symbols (i.e., words) with real objects, thus beginning the development of oral language. However, it must be noted that this is a risky assumption to make, and a large number of other factors will determine whether the child is able to learn to interpret the auditory input provided by a cochlear implant. This is an area that clearly requires further study.

Case Example: A Congenitally Deaf-Blind Child

Michael, a 4-year-old child congenitally deaf and blind as a result of severe prematurity, was assessed for the possibility of cochlear implantation. He attended sessions over a 6-month period, during which time he was seen by a consistent set of implant team members, and established testing/session routines with the various professionals involved. The aims of the psychological assessment were as follows:To this end, Michael's interactions with his parents, implant team members, and educational staff were observed in the clinic, home, and educational settings. Appropriate sections of the Reynell-Zinkin Scales for Young Visually Handicapped Children were administered (social adaptation, sensorimotor understanding, and exploration of environment; Reynell, 1979), bearing in mind that these were not developed or standardized on children with a hearing as well as visual impairment. Although it was not possible to give any instructions to Michael, it was possible to encourage him to explore and manipulate the test materials (primarily everyday objects) and demonstrate through touch what was required of him. Results of the psychological assessment indicated that Michael is a highly enthusiastic explorer of his environment (gaining great satisfaction and enjoyment from dismantling objects and then trying to reassemble them), he shows a high level of communicative intent and symbolic representation through the use of both home-made and official signs and joint attention, he has the ability to persevere with an activity until he has achieved his goal, and he has the ability to transfer learning from one setting and session to another. On this basis, it was felt that there was sufficient positive evidence of learning capacity to recommend cochlear implantation for Michael, from psychological perspectives. Michael received an implant around a year ago and has developed an oral expressive vocabulary of around 25 words and is beginning to join two words together. He understands many more spoken words and is also further developing receptive and expressive vocabulary in sign.

To gain an impression of the child's learning capacity and learning style

To assess his/her ability to adapt to novel situations

To assess the relationship between his/her behavior and learning capacity/style

To gain an impression of the child's motivation to learn and explore

To attempt to establish whether there are learning difficulties over and above those to be expected given the level of dual sensory deprivation

Although the assessment of deaf children with complex needs is a major challenge, it is not the only one to be faced before implantation. Parents who are considering a cochlear implant for their child naturally want to know what degree of benefit their child will receive and the potential risks. For many parents with multiply handicapped children, whose medical problems may put them at greater than normal risk under anesthesia, any potential benefit has to be weighed against the risks. However, as the research literature described above indicates, advising parents on likely outcomes in children with additional disabilities is a very inexact science at this point in time. Communicating the highly complex, often technical information needed to allow parents to make a fully informed decision is a skill that all team members need to acquire. Parents may need help arriving at realistic expectations for their child's potential progress with a cochlear implant. Some parents also need help coming to terms with findings of the assessment, where the extent of the child's difficulties may not have been fully realized previously.

Children Whose Additional Disabilities Are Identified After Implant

A wide variety of disabilities or complex needs can be included in this group: social communication/autistic spectrum disorders, language disorders, auditory processing disorder, dyslexia, dyspraxia, progressive visual impairment, and behavioral or emotional problems. Some difficulties may have been present all along but masked by the deafness (e.g., language disorder or dyslexia), and others (e.g., behavioral or emotional problems) may arise as a result of a variety of factors, related to deafness or coincidental to it. The majority of children who fall into the first category are identified after a period of implant use, often of many years, who are failing to make the progress expected in developing oral and later written language skills, given their age at implant, cause of deafness, nonverbal cognitive abilities, and rehabilitation input. Unfortunately, this group is a very difficult one to identify early, but the problem is often compounded by a "wait and see" attitude by some professionals. Although some children do initially make slow progress, only to "take off" and make rapid progress after a period of time, others seem to remain stuck at the earliest stages of language development or simply make inordinately slow progress. Thus, one of the major challenges for implant programs is to identify these children as early as possible, a process which can now be operationalized using a system of "clinical red flags," raised when a child has not achieved certain benchmarks for average auditory progress at intervals during the first year after implant (Robbins, 2005). The benchmarks were established independently for three groups of children depending on age at implant, residual hearing, communication mode, and hearing aid use prior to implant, based on research findings and clinical experience.

However, having identified that a child is not making the anticipated progress, the next challenge is to identify the reason for this. Although a specific learning disability, communication disorder, or other diagnosable difficulty may play a significant role, other factors must also be taken into consideration. These include family and social issues and educational and rehabilitation input (e.g., speech and language therapy). Where an additional disability is likely, assessment of its nature should be multidisciplinary. In most cases, it is advisable that a psychological assessment is part of the protocol. One of the first steps is always to assess the child's nonverbal cognitive abilities, in order to rule out generalized learning difficulties as a potential cause of the child's problems developing language skills. Having done this, there is a wide range of possible assessment tools available to explore and define the nature of the child's difficulties, the choice of which depends on the presenting difficulties, the age of the child, and pragmatically, whether the child has the language skills necessary to undertake the test in the first place. It must also be remembered that commonly used psychometric tests are not standardized on hearing-impaired children, and only occasionally does the manual include data on a small number of deaf children for comparison. Therefore, such tests must be administered by a psychologist who is experienced in working with deaf children, and even then the results should be interpreted with great caution.

Diagnosis of an additional disability should not be made solely on the basis of results from formal, structured psychometric assessments. Observation of the child in a variety of settings, questionnaires for parents and/or teachers, and semistructured interview schedules can be highly informative, particularly where the problems are, for example, behavioral or emotional, autistic spectrum disorders, or executive function disorder.

Having discovered an additional disability or difficulty, the next challenge may be overcoming family resistance to the diagnosis. Understandably, many parents feel a sense of shock, anger, or grief that their child has yet another hurdle to overcome in terms of reaching his/her potential, and in some cases additional difficulties are vehemently denied. More surprisingly, some professionals working with the child at a local level may also contest the diagnosis of an additional disability, perhaps due to fears that they will not have the skills needed to meet the child's complex needs. It is clear that these children require highly specialized teaching and that at the present time there is often a lack of training or support for teachers and other professionals in this field, working with children who are deaf and have another significant disability. Consequently, the challenge is to raise awareness of the wide variety of presentations and types of difficulties children with cochlear implants may experience and of the need to diagnose and intervene as early as possible. Sometimes the diagnosis confirms the belief held by parents and/or teachers that the child is not placed in the most appropriate educational provision to meet his/her needs. It may be that their hearing impairment is no longer their primary impairment, and their needs could be better met in a school or setting for children with language disorders, social communication disorders, or moderate learning difficulties. This can be challenging both in terms of parental expectations and acceptance and in finding appropriate provision within the area in which the child lives.

Conclusions

The available published literature on outcomes following cochlear implantation in children with additional disabilities suggests that cognitive functioning is one of the strongest predictors of progress in developing speech perception and speech production skills. Unfortunately, this is also one of the most difficult areas to accurately assess before implant in those children with the most complex needs. However, as with diagnosing additional disabilities after implantation, it is not acceptable to avoid these issues simply because they are so challenging. It is only through clinical practice and experience, leading to audit and research, that a sufficiently large body of evidence may be accumulated in order to develop guidelines for best practice and provide parents (and where appropriate children) with the information they need to make an informed choice about cochlear implantation or the most effective interventions for children requiring additional support to achieve their potential. Currently, there is no research that compares the outcomes of children with complex needs using cochlear implants with children with similar disabilities but who use conventional hearing aids. It could be argued that for cochlear implantation to be appropriate in children with known additional disabilities, there should be demonstrable differences in outcome between these two groups.

At a broader, more philosophical level, we need to consider what the aims and objectives are when offering a cochlear implant to a child (and their family) who has multiple disabilities. At what level are we hoping for change-speech perception, communication, language, educational attainment, or employment? Alternatively are the priorities in psychological well-being, mental health, or quality of life? How do we define these concepts and operationalize the changes hoped for or realized after implantation? Finally, who should be the judge of whether the intervention was successful-cochlear implant professionals, parents, or the children themselves? These are all questions for future study.

No conflicts of interest were reported.

References

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Children With Cochlear Implants and Complex Needs: A Review of Outcome Research and Psychological Practice | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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This article will focus on a special group of children with cochlear implants: those who have difficulties or disabilities in addition to their deafness which make the task of meeting their complex needs particularly challenging. A figure of 30%-40% of deaf children is consistently quoted as being the proportion with additional disabilities, although definitions of what constitutes an additional disability do vary.

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factors effecting the development of speech

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Development / Hearing Aids, Cochlear Implants & Assistive Technology / School-Based Settings / Normal Language Processing / Speech, Voice & Prosody / Clinical Forum: Oral Communication in Children With Cochlear Implants

Clinical Forum

Factors Affecting the Development of Speech, Language, and Literacy in Children With Early Cochlear Implantation

Language, Speech, and Hearing Services in Schools , July 2002, Vol. 33, 172-183. doi:10.1044/0161-1461(2002/015)

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Factors Affecting the Development of Speech, Language, and Literacy in Children With Early Cochlear Implantation

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Purpose: This study investigated factors contributing to auditory, speech, language, and reading outcomes in children with prelingual deafness after 4-6 years of multichannel cochlear implant use. The analysis controlled for the effects of child, family, and implant characteristics so that educational factors most conducive to maximum implant benefit could be ...

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deaf childrens use of phonological coding

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Deaf Children's Use of Phonological Coding: Evidence from Reading, Spelling, and Working Memory | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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Abstract. Two groups of deaf children, aged 8 and 14 years, were presented with a number of tasks designed to assess their reliance on phonological coding. The

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speech reading and learning to read

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Abstract

Nine children with severe-profound prelingual hearing loss and single-word reading scores not more than 10 months behind chronological age (Good Readers) were matched with 9 children whose reading lag was at least 15 months (Poor Readers). Good Readers had significantly higher spelling and reading comprehension scores. They produced significantly more phonetic errors (indicating the use of phonological coding) and more often correctly represented the number of syllables in spelling than Poor Readers. They also scored more highly on orthographic awareness and were better at speech reading. Speech intelligibility was the same in the two groups. Cluster analysis revealed that only three Good Readers showed strong evidence of phonetic coding in spelling although seven had good representation of syllables; only four had high orthographic awareness scores. However, all 9 children were good speech readers, suggesting that a phonological code derived through speech reading may underpin reading success for deaf children.

Although the great majority of children who are born with or acquire a profound hearing loss in the first year of life experience considerable difficulty in learning to read ( Marschark & Harris, 1996; Musselman, 2000) and write ( Singleton, Morgan, DiGello, Wiles, & Rivers, 2004), a small number become highly proficient. Relatively little research has been carried out on these children and, in part, this is because there are relatively few. In a longitudinal study of beginning readers ( Harris & Beech, 1998), only 4 children out of 24 were reading at a level comparable to hearing peers after 2 years of reading instruction; and in a later study, using a different cohort, only 6 children out of 29 had a reading age within 6 months of their chronological age of 8 years ( Harris & Moreno, 2004). As Marschark and Harris (1996) note, this low proportion of deaf children who learn to read successfully has been consistently reported since the first systematic assessments were carried out in the United States ( DiFrancesca, 1972) and the United Kingdom ( Conrad, 1979).

What evidence is there about how deaf children become successful readers? A useful starting point is to consider how the majority of children (i.e., those who are hearing) learn to read. A common view is that learning to read for hearing children draws heavily upon spoken language skills, especially phonological awareness. The prevalence of this view is captured in a recent review ( Castles & Coltheart, 2004) that documents 44 longitudinal studies examining the relationship between phonological awareness and learning to read. As Castles and Coltheart (2004) argue, there remains considerable uncertainty about how the various aspects of phonological awareness and phonological coding are related to each other, and to reading success. However, there is agreement about one key aspect of learning to read:

There is a vast amount of educational and psychological research to suggest that knowledge about letter-sound correspondences is a primary building block to reading, providing children with the tools to sound out new words and assisting with the formation of lexical representations. (Castles & Coltheart, 2004)

One possibility is that deaf children who have good phonological awareness and good phonological coding skills can learn to read in the same way as hearing children by developing their knowledge about letter-sound correspondences. A number of studies have shown that phonological awareness and phonological coding skills are strong predictors of reading success among deaf children ( Dyer, MacSweeney, Szczerbinski, Green, & Campbell, 2003; Harris & Beech, 1998; Harris & Moreno, 2004). This relationship holds even though deaf children, as a group, show less evidence of phonological coding than hearing peers ( Beech & Harris, 1997; Burden & Campbell, 1994; Harris & Beech, 1998; Harris & Moreno, 2004; Leybaert & Alegria, 1995; Merrills, Underwood, & Wood, 1994; Nielsen & Luetke-Stahlman, 2002; Waters & Doehring, 1990). Taken together, these studies suggest that good deaf readers are likely to have better phonological coding skills than deaf children who are poor readers. However, Harris and Beech (1998) found that this was not the case for all 4 good readers in their sample, and they report that two different profiles were evident. The best reader, who did not sign, had good spoken English and did well on a test of phonological awareness; however, the child with the second highest score was a native signer (of Deaf parents) who had poor phonological skills; and of the other 2 good readers, one showed the first pattern and one, the second. Harris and Beech conclude, "The presence of these two contrasting profiles underlines the fact that deaf children may become successful readers by more than one route" (p. 214).

Comparisons of the reading strategies of orally educated children and those whose preferred language is sign ( Miller, 2002) suggest that communication mode has a profound effect on the extent to which children who are deaf make use of phonological coding in reading. Miller (2002) argues that children who are native signers do not engage in phonological recoding. At the same time, some authors have claimed that knowledge of a sign language (usually American Sign Language [ASL]) promotes the development of reading and spelling ( Wilbur, 2000) and that deaf children with high levels of proficiency in ASL have higher levels of English literacy than peers of similar IQ but with lower levels of ASL ( Strong & Prinz, 1997). However, it is not clear exactly how good signing promotes good reading, especially in the early stages of learning to read, and an authoritative review of literacy attainment in deaf children who sign ( Goldin-Meadow & Mayberry, 2001) concludes that skill in signing does not guarantee skill in reading. Goldin-Meadow and Mayberry (2001) point to the need for researchers to uncover how deaf readers could map their knowledge of sign onto print.

Although it has been suggested that early exposure to sign is an important factor in educational achievement ( Schlesinger & Meadow, 1972), more recent research suggests that deaf children learn to read best when they are exposed to both signing and oral language (see Marschark & Harris, 1996, for a review). The suggestion is that good signing skills provide children with a secure language base, whereas knowledge of the oral language provides them with an understanding of the sounds that occur in the words they will encounter in reading. If this is the case, then we would expect to see that successful deaf readers share certain skills in common and that, rather than there being two distinct routes to reading success for deaf children, there might be a single route in which certain components have more or less emphasis in individual children.

The aim of this study was to identify the skills of good deaf readers. We did this by making a direct comparison between good and poor deaf readers, selected from a larger sample, in order to discover how their reading-related skills differed. A number of comparison measures were used. The first two, which looked directly for evidence of phonological coding in spelling, were percentage of phonetic errors and percentage of errors in which the number of syllables was correctly represented. The other measures were orthographic awareness, speech intelligibility, and accuracy in speech reading.

There was a clear rationale for the inclusion of each of these measures. The percentage of phonetic spelling errors was designed to uncover how much deaf children were aware of the sounds in words that they could not spell accurately. When hearing children attempt to spell an unfamiliar word in an irregular orthography such as English, where a sound may be spelled in a number of different ways, they often choose the correct sound but select an incorrect way of spelling it as in PLAD for plaid or BRUTR for brother ( Trieman, 1998). Deaf children, as a group, show a different pattern. French-speaking 11-year-old deaf children made few phonetic spelling errors even though hearing French children commonly produce such errors ( Leybaert & Alegria, 1995), and a similar pattern has emerged from studies of English ( Padden, 1993; Sutcliffe, Dowker, & Campbell, 1999). Sutcliffe et al. (1999) found that deaf children frequently omitted consonants-a pattern that was seldom seen in the spelling of the hearing children. Analysis of the spelling errors produced by deaf adults ( Olson & Caramazza, 2004) also shows that the majority are phonologically implausible although orthographically legal (e.g., MEDINCE for medicine and SUBITUSE for substitute). We expected Good Readers to produce more phonetic errors than Poor Readers.

The second spelling measure was also concerned with the representation of sound but at the syllabic rather than the phonemic level. Our interest in this level of representation followed from the claim that deaf children have syllabic knowledge at a level comparable to hearing children of the same reading age (Sterne & Goswami, 2000). If this is the case, we might expect to find that Good Readers are able to correctly represent the number of syllables that occur in a word they cannot spell correctly even though they may not be aware of individual phonemes.

The orthographic awareness test was designed to see whether Good Readers had a better grasp of English orthography than Poor Readers. Deaf adults respect the rules of orthography in spelling ( Olson & Caramazza, 2004); and performance on a task, in which children have to discriminate between legal and illegal letter strings, significantly predicted the reading level for both 8- and 14-year-old deaf children ( Harris & Moreno, 2004). We therefore predicted that Good Readers would score more highly on orthographic awareness than Poor Readers.

We also included a rating of speech intelligibility because some previous studies have shown that deaf children with more intelligible speech become better readers ( Harris & Beech, 1998; Leybaert & Alegria, 1995). However, because at least some children with good signing skills but relatively poor oral skills become good readers ( Strong & Prinz, 1997; Wilbur, 2000), it was important to assess this measure.

The final measure was accuracy in silent speech reading (also known as lipreading). A number of authors have claimed that deaf children can acquire a phonological code through speech reading ( Alegria, 1998; Alegria, Charlier, & Mattys, 1999; Campbell, 1997; Campbell & Burden, 1994; Dodd, 1980). However, lip patterns are often ambiguous and some sounds (e.g., the bilabials /b, m, p/) are almost impossible to distinguish on lip pattern alone. Speech reading thus provides, at best, an impoverished version of the code that hearing children have when they first learn to read.

Studies of Cued Speech (CS)-a system that uses hand shapes made near the mouth to disambiguate sounds with the same lip pattern-have found that CS can improve deaf children's ability to lip-read at least in French ( Alegria, 1998; Alegria et al., 1999). Deaf children who receive CS from an early age are able to make decisions about whether words rhyme at a level comparable to hearing peers ( Leybaert & Charlier, 1996) even when spelling cannot be used as a guide as in tasse-glace. This contrasts with deaf children, not exposed to CS, who find rhyme judgments very difficult ( Harris & Beech, 1998). A recent study ( Alegria & Lechat, 2005) has shown that deaf children integrate information from cues and lip patterns when they perceive words, and this supports the idea that the additional information provided by the cues is being used to produce finer discriminations. Although none of the children in this study had been exposed to CS (which is not commonly used in the United Kingdom), we expected that they would vary considerably in their speech reading ability ( Dodd, McIntosh, & Woodhouse, 1998). If speech reading can be used to develop a phonological code, we would expect to find that Good Readers were better at speech reading than Poor Readers.

Method

Participants

The Good and Poor Readers were selected from an initial cohort of 29 deaf children described in Harris and Moreno (2004). The children were aged between 7 and 8 years at initial assessment and were recruited from a total of 10 schools and units for deaf children of primary school age. All had an unaided hearing loss of at least 85 dB in the better ear and a nonverbal IQ above 85, assessed using the Matrices, Recall of Designs, and Block Design tests from the revised British Ability Scales II (BASII) ( Elliott, Smith, & McCulloch, 1996). Although there was wide variation in educational regime, most children used Total Communication in the classroom in which spoken English and Signed English were used together. The signing skills of the children were very variable, with a small number being fluent users of British Sign Language (BSL). However, it is notable (see Table 7) that 5 children in the Good Reader group had BSL as their first language, whereas this was true of only one child in the Poor Reader group.

At initial assessment, the children were tested for reading using the single-word reading test from BASII. Children chose either to read aloud or sign or use a mixture of both when producing their responses, and in the case of oral responses, children were not penalized for pronunciation. Nine children were identified as Good Readers in that they were reading within 10 months of their chronological age (see Table 7 for individual lags). Nine children were identified as Poor Readers in having a reading lag of at least 15 months below chronological age. In the worst case the delay was 31 months behind chronological age and in the best it was 15 months. There was a highly significant difference between the mean reading age of the two groups, which was 96 months (8;0) for the good readers and 70 (5;10) months for the poor readers (see Table 1). The two groups did not differ in chronological age, nonverbal IQ, or unaided hearing loss (see Table 1) and both comprised children who were native users of BSL as well as those with poor signing skills (see Table 7).

Assessments

Reading comprehension.

In addition to the single-word reading test used for initial screening, children were also given the Neale analysis of reading ability (Neale, 1989) to assess their level of reading comprehension.

Spelling test.

The spelling test was as reported in Harris and Moreno (2004) and consisted of 39 line drawings of familiar objects. Children were asked to spell the name of each picture in turn. Items were selected to produce a large number of spelling errors so that, in addition to some common monosyllabic regular words such as car and fish and some monosyllabic irregular words (e.g., eye and door), there were several multisyllabic items such as television, aeroplane, telephone, giraffe, and squirrel. Incorrect spelling was classified as containing a phonetic error if at there was at least one instance where a sound was incorrectly represented by a letter or letters that sounded correct (e.g., BATERRE, BATTARY, BATRY, BATIRY, BACHREY were all classed as phonetic errors).

In addition to phonetic errors, incorrect spellings were classified according to whether or not a child attempted to represent the number of syllables of the target word. Thus spellings such as JRAFF, DARAFE, and JRUF were classed as containing two syllables (like giraffe) but GITF, DRUF were not.

For both classifications, there were two independent raters. Cases of disagreement were infrequent, but where they occurred, there was an agreed classification for each item.

Orthographic awareness test.

Full details of this task can be found in Harris and Moreno (2004) where data for the original cohort are reported. The task ( Siegel, Share, & Geva, 1995) presents children with legal and illegal letter strings and asks the child to chose the legal one. Illegal strings contained a bigram that never occurs in English in a given position as in filv. In order to make the task appealing to children we used pictures of aliens and, through a series of training trials, explained that the idea was to distinguish between a name that seemed very strange (illegal strings) and one that seemed like an English name (legal string). There were then 17 trials, each with four items in which one was a legal string and the others were illegal.

Speech intelligibility.

The Speech Intelligibility Rating, developed for the evaluation of cochlear implantation in children ( Allen, Nokolopoulos, Dyar, & O'Donoghue, 2001), was used to assess intelligibility. The scale has five categories, ranging from connected speech being intelligible to all listeners to no words being intelligible even to someone familiar with the child (see Table 2). The second author (who was very familiar with the children and could speech read) carried out an initial rating. Then an independent rater, who was not familiar with the speech of deaf children, carried out rating from videorecordings made during initial assessment. In most cases, the two raters awarded the same category but, if they did not, the rating of the inexperienced rater was used to assign Categories 4 and 5 because these require intelligibility by all listeners rather than someone who is familiar with the children.

Category

Speech intelligibility description

5

Connected speech is intelligible to all listeners. The child is understood easily in everyday contexts

4

Connected speech is intelligible to a listener who has had little experience of a deaf person's speech. The listener does not need to concentrate unduly

3

Connected speech is intelligible to a listener who concentrates and lip-reads within a known context

2

Connected speech is unintelligible. Intelligible speech is developing in single swords when context and lipreading cues are available

1

No words can be recognized

Speech reading.

Speech reading ability was assessed by a test, devised by the authors, in which children were presented with a set of pictures from which they had to select one that corresponded to a word or phrase being pronounced by a female speaker on a silent videorecording. There were five blocks of single words, involving discriminations of increasing difficulty ( Geers, 1994). In the first block, items differed in number of syllables as well as onset and rime (e.g., fish, baby, birthday cake); and in the second block all items were two syllables long but still differed in onset and rime (e.g., flower, sandwich, carrot). Blocks 3, 4, and 5 were considerably more difficult in that all words were monosyllabic. In Block 3, words had the same onset (e.g., boat, ball, book) and, in Blocks 4 and 5, they all had the same rime (e.g., hair, square, chair in Block 4 and tree, knee, ski in Block 5). There were 10 items to speech read in each block with some being taken from Geers (1994) and others chosen by the authors. Each word was pronounced twice, and children pointed to the correct picture after hearing each word. In each block, some items were repeated so that the child could not exclude pictures that had previously been chosen.

The final part of the speech reading test was an adaptation of the early part of the Verbal Comprehension test from the British Ability Scales ( Elliott et al., 1996). This involves presentation of simple commands and questions involving a picture and a set of toys. As for the single words, each item was pronounced twice and responses were scored as correct if children responded appropriately. (See Appendix for a list of the items used in the speech reading test.)

Procedure.

All testing was carried out individually in the child's school. The tests of reading comprehension, spelling, and orthographic awareness tests were administered several weeks after the initial screening. The speech reading test was administered in follow-up testing that took place within 1 year of initial assessment.

Results

In order to establish that the difference between the Good and Poor Readers extended beyond the domain of single-word reading, scores for the two groups were compared on the Neale test and the spelling test (see Table 3). It can be seen that there was considerable difference between the two groups on both measures with the good readers scoring more highly. On the Neale test, none of the Poor Readers scored below the cutoff point, whereas all the Good Readers did so. This difference was highly significant ( t ₁₆ = 5.03, p = .001). The difference between scores on the spelling test was also highly significant ( t ₁₆ = 4.83, p < .001).

Table 4 shows the intercorrelation of the five measures that we used, controlling for nonverbal IQ. The partial correlation matrix shows significant correlations between the percentage of phonetic errors and both correct representation of syllables ( r = .80, p < .001) and orthographic awareness ( r = .46, p = .049). Speech reading was also significantly correlated with phonetic errors ( r = .51, p = .025), and the correlations with both orthographic awareness ( r = .44, p = .059) and syllabic representation ( r = .44, p = .058) approached significance. Speech intelligibility was not, however, correlated with any of the other measures.

Because phonetic errors and speech reading were correlated with each other and with the other measures, hierarchical regression was used to compare their relationship to reading. Two forced-entry regression analyses were carried out, with phonetic errors and speech reading as the predictor variables and reading lag (in months) as the dependent variable. The results are summarized in Table 5 where it can be seen that speech reading remained a significant predictor even after percentage of phonetic errors was entered as the first term, whereas the same did not hold when phonetic errors was entered as the first term and speech reading as the second.

Table 6 shows the mean scores of the Good and Poor Readers on each measure. It can be seen that Good Readers made significantly more phonetic errors and were more accurate at representing syllables than Poor Readers. They also had higher scores in the tests of orthographic awareness and speech reading (both for single words and for sentences). Independent t tests revealed that all these differences were significant at p ≤ .01 (see Table 6 for t values). Speech intelligibility ratings were, however, identical for the two groups.

At first sight, these results imply that Good Readers differed from Poor Readers on every measure except speech intelligibility. However, there was wide individual variation even for the four measures where there were clear group differences: phonetic errors ranged from 0% to 40%, correct representation of syllables from 25% to 89%, orthographic awareness scores from 4 (below chance) to 11, and speech reading scores from 35 to 43. (See Table 7 for individual scores of Good Readers and Poor Readers.)

In order to shed further light on individual scores, k-means cluster analyses (using two clusters, Group 1 = higher scores, Group 2 = lower scores) were performed on each measure, except speech intelligibility, to see whether it could reliably distinguish Good and Poor Readers. Cluster analyses of this kind are appropriate when the number of groups is small and is known a priori ( Beauchaine & Beauchaine, 2002).

For phonetic errors, all children assigned to Group 1 ( n = 3, cluster center = 33.44) were Good Readers, but Group 2 ( n = 15, cluster center = 3.60) contained 6 Good Readers as well as all the Poor Readers. The percentage of correct representation of syllables produced two equally sized clusters: Group 1 ( n = 9, cluster center = 58.05) contained 7 Good Readers and 2 Poor Readers and Group 2 ( n = 9, cluster center = 19.09) showed the reverse pattern, suggesting that this measure discriminated better between the two groups of readers than phonetic errors. For orthographic awareness, Group 1 ( n = 7, cluster center = 9.43) contained 5 Good Readers and 2 Poor Readers and Group 2 ( n = 11, cluster center = 3.73), 4 Good Readers, and 7 Poor Readers. For speech reading, the division was Group 1 ( n = 15, cluster center = 36.60) and Group 2 ( n = 3, cluster center = 19.33). This pattern was rather different from the other measures in that all the Good Readers were assigned to Group 1 as well as 6 of the Poor Readers.

Discussion

The aim of this research was to identify characteristics of children who are prelingually, profoundly deaf but who, nevertheless, learn to read successfully. Before discussing the findings, it is important to note that the two groups of children that we identified differed not only in single-word reading but also in their ability to read text and to spell. We were thus comparing two carefully matched groups of similar chronological age that had extensive difference in reading and spelling.

The five measures we chose for comparison were selected because they assessed a number of different skills that had been previously identified as potentially important for reading success in deaf children. Five of the measures-phonetic errors, syllabic representation, orthographic awareness, and speech reading of words and sentences-were significantly higher for Good Readers than Poor Readers. Only one measure-speech intelligibility-was not. As can be see in Table 6, Good Readers had ratings that ranged from 5 ( all words intelligible) to 1 ( no words intelligible). The children in our sample did not, therefore, show the pattern reported in other studies of a link between speech intelligibility and reading success ( Harris & Beech, 1998; Leybaert & Alegria, 1995). It is worth noting, that this failure to find a difference in intelligibility between the two groups is unlikely to be because our measure was not discriminating since Harris and Beech (1998) used only a 3-point scale.

The pattern of intercorrelations among measures suggests that they were tapping skills with common components. Not surprisingly, the highest correlation (.80) was between the two measures of phonological coding in spelling-phonetic errors and syllabic representation. In part, the strength of this correlation lies in the fact that both measures were derived from spelling errors. Nevertheless, it also seems likely that the two measures reflected a common encoding ability because representation of sounds at both the phonemic and the syllabic level requires a good knowledge of letter-sound correspondences. There was also a significant correlation between phonetic errors and orthographic awareness, again suggesting that letter knowledge was a core skill. This knowledge was not, however, related to the intelligibility of children's own speech. Indeed, speech intelligibility was not related to any of the other measures that we used, and, as we have noted, it did not discriminate Good and Poor Readers.

Whereas speech intelligibility was not correlated with any other measures, speech reading was correlated with phonetic errors and, in the case of both syllabic representation and orthographic awareness, correlations approached significance. Percentage of phonetic errors was also correlated with all other measures apart from speech reading. On the basis of the correlations, then, either speech reading or phonetic errors could be measures of a core skill that underpinned the other abilities. From a theoretical perspective, however, the more likely interpretation is that speech reading ability is the core skill. This would be in line with earlier arguments about speech reading as an important mechanism by which deaf children acquire representations of phonology ( Alegria et al., 1999; Campbell, 1997; Dodd, 1980). Our data suggest two lines of evidence in support of this view.

The first comes from the results of the k-means cluster analyses. These showed that, in spite of significant group differences, our measures did not always separate the two groups of readers. On phonetic errors, only 3 Good Readers fell into the higher group and 6 could not be distinguished from Poor Readers. For syllabic representation, the majority of Good Readers fell into the higher group but 2 did not; and for orthographic awareness 5 did and 4 did not. The only measure on which all Good Readers were assigned to the higher group was speech reading, showing that, whereas some Good Readers had good phonological skills and some had good orthographic skills, they were all good at speech reading.

Before accepting this conclusion it is pertinent to consider why cluster analysis also placed a majority of Poor Readers in the higher scoring group on speech reading. Table 6 shows that the standard deviation of speech reading scores was considerably lower for Good Readers (2.57) than Poor Readers (7.68) in spite of the fact that the mean score was significantly higher for the Good Readers. This unusual pattern, in which a lower mean was associated with a higher standard deviation, reflects the fact that the scores of Good Readers ranged between 35 and 43 whereas, among the Poor Readers, the highest score was 40 but the lowest was 17. In fact, only two Poor Readers had a speech reading score of 35 or more (i.e., a score that equaled those of Good Readers), so there was less overlap between the groups than the Cluster analysis might suggest. Interestingly, one of these children was the best of the Poor Readers with a reading lag of 15 months, whereas the other-the one with a speech reading score of 40-had a reading lag of 22 months. He had received a cochlear implant at 4 years and was making good use of his hearing, so it is possible that his reading ability would have improved with time to make it more commensurate with his level of speech reading.

The second line of evidence for the importance of speech reading comes from regression analyses. In order to compare the power of phonetic errors and speech reading to predict reading lag, two contrasting forced-entry regression analyses were carried out (see Table 5). When speech reading was entered as the first variable, R ² change = .47 ( p = .002). The addition of phonetic errors resulted in R ² change = .07 ( p = .16). In other words, the percentage of phonetic errors did not predict reading lag once speech reading was accounted for. When the percentage of phonetic errors was entered as the first variable, R ² change = .38 ( p = .006). Entering speech reading as the second variable resulted in R ² change = .16 ( p = .040). In other words, speech reading was a still a significant predictor of reading lag even when the percentage of phonetic errors had been accounted for.

Both cluster and regression analyses of our data point to speech reading as the core skill that underpins the capacity for phonological representation. This supports arguments that have been made about orally educated deaf children ( Alegria et al., 1999; Leybaert & Charlier, 1996). However, it is notable that 5 of the Good Readers in our sample had deaf parents and were native signers for whom BSL was their first language. In all these cases, however, it was reported that English was also used at home (see Table 7), supporting the argument of Marschark and Harris (1996). The other 4 children had hearing parents and, of these, 2 had very little signing, one had learned BSL at school, and the other had been exposed to BSL from birth. All 4 of these children had received cochlear implants.

In spite of their heterogeneity, all children identified as Good Readers were good speech readers. There has been considerable debate about the relationship between signing and speech reading ( Mogford, 1987), and some studies have shown that the use of manual communication is associated with less proficient speech reading ( Bernstein, Demorest, & Tucker, 1998). In should be noted, however, that it is not always easy to interpret findings about the relationship between signing and speech reading because decisions about the educational environment for deaf children of hearing parents are often made in the light of deaf children's early oral abilities. In other words, signing may be introduced to children who are failing to acquire language from oral input ( Harris & Beech, 1998). The finding that children with Deaf parents, who are exposed to sign from birth and use it as their preferred language, can become good speech readers strongly suggests that competence in one modality does not preclude competence in the other. Indeed, we would suggest that, even for children who are competent signers, skill in speech reading provides an important basis for the development of a phonological code that can be used in learning to read.

It would appear, however, that Good Readers drew on their speech reading skills in different ways. Some children (notably Participants 1, 5, 7, and 8 in Table 7) appeared to be using phonological coding in a similar way to hearing children as evidenced by phonetic errors in spelling and correct representation of syllables. Other children (notably Participants 2, 4, and 9 in Table 7) appeared to be making little or no use of phonological coding because they made few or no phonetic errors; and this suggests that their reading may have relied more on coding at a whole-word level.

Within the group of Poor Readers, there was also considerable heterogeneity (see Table 7). Four of the children in this group had received a cochlear implant and 1 had a deaf mother. Three were reported as learning to sign at preschool and 3 learned to sign at primary school. The other 3 children did not sign. Intriguingly, 2 children in the group (Participants 10 and 11) had speech reading scores that were comparable to those of the Good Readers as noted above. This strongly suggests that speech reading ability does not on its own lead to reading success. Other factors, for example, good knowledge of English vocabulary, are also likely to be important. Further research is required to establish how speech reading skills interact with other abilities as deaf children learn to read. Early speech reading skills have been shown to predict developing spoken and signed language skills ( Dodd et al., 1998), so one might expect there to be a complex developmental trajectory in which speech reading has both direct and indirect effects on the reading progress of deaf children.

Finally, if the conclusions of this study are correct, they throw up an intriguing question: What kind of code do deaf children derive from speech reading? As others have argued ( Alegria, 1998; Alegria & Lechat, 2005; Campbell, 1997), there appears to be an abstract phonological code that is common to both listening and speech reading. Indeed, speech perception for hearing people involves the integration of information from the sounds and lip movements made by a speaker. However, in some sense the code used by deaf children is different from that used by hearing children because it reflects distinctions that are unique to speech reading. One possibility is that the code is articulatory and that what is being encoded is the movement of the articulators.

Studies of adults have shown that lip movements, together with visibility of the tongue and teeth, provide the most useful information for speech reading ( Summerfield, 1979; Summerfield, McLeod, McGrath, & Brooke, 1989); so it is likely to be these aspects that are encoded. However, it is important to emphasize that in speech perception information of this kind is normally integrated with auditory information right from birth ( Locke, 1993), and even where children have very little access to auditory information, they will bring these two components together. Recent work on speech reading in children who have received a cochlear implant has shown that implanted children were better at speech reading when they were presented with both visual information (i.e., tongue and lip movements) and sound than when they were presented with only one modality ( Bergeson, Pisoni, & Davis, 2005). The authors emphasize the "coupling between auditory and visual sensory systems and the potential for unity and convergence ... between these two different sensory systems" (p. 161).

In this study, there was no difference in the unaided hearing loss of the Good and Poor Readers. However, as we did not have sufficient information about aided losses, we were not able to compare these in the two groups. It is certainly possible that the Good Readers may have been able to make better use of their aided hearing and that this, in conjunction with their speech reading ability, was allowing them access to better phonological coding than the Poor Readers. It is interesting to note, however, that in the Bergeson et al. (2005) study, it was performance in speech reading where there was no sound that best predicted speech and language development after implant. This suggests, that for children who are profoundly deaf, the visual component of speech reading is an especially important skill.

In spite of evidence for the utility of CS for deaf children learning French, the evidence for children learning English is less convincing. Alegria and Lechat (2005) note some differences between French and English, especially in the higher incidence of diphthongs in English, that may make CS less useful for English.

Unequal variances were assumed for this comparison.

In view of the number of comparisons that were made, it was decided to set p = <.01 as the criterion for significance, using a one-tailed test.

References

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Speech Reading and Learning to Read: A Comparison of 8-Year-Old Profoundly Deaf Children With Good and Poor Reading Ability | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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Nine children with severe-profound prelingual hearing loss and single-word reading scores not more than 10 months behind chronological age (Good Readers) were matched with 9 children whose reading lag was at least 15 months (Poor Readers). Good Readers had significantly higher spelling and reading comprehension scores.

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congruent correlations

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Abstract

In contrast to the converging findings from hearing children, fairly equivocal findings have emerged from similar studies of reading in deaf children. Although there have been many studies looking at which skills are associated with reading ability, few have considered more than one ability in the same study, and so they do not show the relative contributions of each skill. As with hearing children, the main concern has been with the role of phonological awareness and with two related questions: firstly, whether deaf children and adolescents can be considered to be phonologically aware and, secondly, whether performance on tasks that measure phonological awareness is associated with reading and spelling ability.

Turning to the existence of a relationship between phonological awareness and reading and spelling ability in deaf children and adolescents, only three studies report strong relationships. Harris and Beech (1998) found a positive association (.43) between phonological awareness and progress in reading ability over 1 year in beginning deaf readers. Likewise, Campbell and Wright (1988) observed a correlation of .54 between accuracy on the incongruent items of a rhyme judgment task and reading age and Dyer et al. (2003) found that reading age was related to accuracy on a rhyme judgment task (.39) and pseudohomophone matching task (.46). However, many other authors have not found a significant relationship ( Hanson & Fowler, 1987 ; Hanson & McGarr, 1989 ; Izzo, 2002 ; Leybaert & Alegria, 1993 ; Miller, 1997 ).

Some of these differences in results can be explained by the tasks used and the abilities of the participants. For example, although Izzo (2002) found no association between phonological awareness and reading, the measure of reading was a story-retelling task, which makes use of a number of high-level skills including the ability to summarize and memorize. Additionally, there were severe floor effects in the phonological awareness task in Izzo's study, which may have precluded a significant association. Neither of the Hanson studies ( Hanson & Fowler, 1987 ; Hanson & McGarr, 1989 ) found a relationship between reading and phonological awareness in college-age deaf students, who were already comparatively good readers and well educated, and this may suggest that phonological ability is only related to reading ability in the earlier stages of development. An alternative explanation, suggested by Musselman (2000) , is that phonological ability predominately develops as a consequence of learning to read in deaf individuals rather than being more of a prerequisite, as in hearing children. This interpretation is consistent with the conclusion that the strongest evidence of phonological awareness comes from studies involving older deaf adolescents and college students.

As there is no clear evidence of the role of phonological awareness in deaf children's literacy, other possible correlates of reading ability have been investigated. Language delay has been described as being a hallmark of deafness ( Musselman, 2000 ), and many studies have reported that deaf adolescents have poorer vocabulary knowledge than would be expected for their age or in comparison with hearing adolescents (e.g., Geers & Moog, 1989 ; Moores & Sweet, 1990 ; Waters & Doehring, 1990 ). Taking this into consideration, along with the importance of language for typically reading hearing children, it is unsurprising that language and vocabulary knowledge have been found to be strong correlates of reading ability in deaf children and adolescents ( Harris & Beech, 1998 ; Izzo, 2002 ; LaSasso & Davey, 1987 ; Moores & Sweet, 1990 ; Waters & Doehring, 1990 ).

Several studies have reported strong correlations between speechreading (silent lipreading) and reading in deaf adolescents ( Arnold & Kopsel, 1996 ; Campbell & Wright, 1988 ; Cue, 1996 ; Geers & Moog, 1989 ). For example, Arnold and Kopsel (1996) found that speechreading ability was the only significant correlate of reading ability in 10 orally educated severely and profoundly deaf children, although they did not find a significant relationship for 10 bilingually educated deaf children. The strongest evidence to date, of the importance of speechreading skills for reading ability, comes from a recent study of good and poor deaf readers by Harris and Moreno (2006) . The authors found that speechreading ability was the only skill that correctly identified all the good deaf readers (8-year olds). However, although it clustered all the good readers together, it also included the majority of the poor readers. Thus, although all the good readers had good speechreading skills, it appeared that many of the poor readers also had good speechreading skills, despite being poor readers, suggesting that good speechreading skills alone are not sufficient for success in learning to read.

Short-term memory has also been found to be both a strong correlate and predictor of reading ability in older deaf children and adolescents ( Daneman, Nemeth, Stainton, & Huelsmann, 1995 ; Harris & Moreno, 2004 ; MacSweeney, 1998 ; Waters & Doehring, 1990 ); however, this relationship appears to be dependent upon the age of the deaf participants as no significant association was found in deaf 7- and 8-year olds.

The present study compared two groups of deaf and hearing children, matched on reading age, on a range of reading, spelling, cognitive, and language-based tasks to examine similarities and differences in levels of performance and in the correlates and predictors of literacy. As the majority of previous studies have been correlational in design, this study will also investigate the predictors of literacy achievement in deaf children. The research questions of interest were as follows: How would deaf and hearing children compare in their performance on cognitive and language-based skills hypothesized to be important for literacy? What are the correlates of reading and spelling achievement in deaf school-age children, and which skills account for the individual differences observed in literacy ability? Do deaf children show the same pattern of correlates and predictors of reading and spelling ability as hearing children?

The current study also provided an opportunity to examine whether several experimental effects, previously reported in separate studies of deaf adolescents, could also be observed in the same group of deaf school children from mixed language backgrounds. It has been reported that deaf adolescents are sensitive to spelling-to-sound regularities when spelling as they are more accurate on items for which the spelling is consistent ( Burden & Campbell, 1994 ; Leybaert & Alegria, 1995 ; Sutcliffe, Dowker, & Campbell, 1999 ). Likewise, it has also been observed that deaf children and adolescents are more accurate in making phonological judgments when the orthography and phonology of the word are congruent such as train-rain than when the items are orthographically incongruent such as eye-fly ( Campbell & Wright, 1988 ; Hanson & Fowler, 1987 ; Hanson & McGarr, 1989 ; Miller, 1997 ; Sterne & Goswami, 2000 ). These experimental manipulations were also examined in the current study.

Method

Participants

Twenty-nine 7- and 8-year-old deaf children (12 boys and 17 girls) and 31 reading-age matched hearing children (13 boys and 18 girls) participated in the study. The deaf children were recruited and tested initially, and then the hearing children were selected to match the deaf group on reading age (for single words in isolation) using the Single Word Reading subtest from the British Ability Scales II (BAS II; Elliot, Smith, & McCulloch, 1996 ). Written parental permission was given for all participating children.

Deaf children.

The deaf children ranged in age from 6 years 8 months to 8 years 7 months with a mean age of 7 years 10 months ( SD = 7.0 months). The mean reading age for the deaf children, using the BAS II Single Word Reading subtest ( Elliot et al., 1996 ), was 6 years 10 months ( SD = 9.5 months). The criteria for inclusion were that all deaf children (1) were prelingually deaf (before the age of 2 years); (2) had a severe or profound hearing loss of greater than 71 db; (3) had a nonverbal intelligence (NVIQ) composite score of at least 85 (estimated using three subtests from the BAS II; Elliot et al., 1996); and (4) had no additional cognitive, social, or significant behavioral problems. The mean hearing loss for the group was 99 db ( SD = 11.0) and ranged from 71 db to greater than 120 db. All the children wore hearing aids, and seven were fitted with cochlear implants. Nine of the children (31%) had at least one deaf parent (Deaf of Deaf), and the other 20 had hearing parents (Deaf of Hearing).

The sample is representative of the diversity of educational settings in which deaf children are taught in England ( Gregory & Hindley, 1996 ). Nine different specialist schools for the Deaf or hearing-impaired units attached to mainstream schools around the South East of England participated. The children varied considerably with respect to the communication method through which they were taught (bilingual, total communication, or oral), the communication method used at home, their language preference, and their signing skills. Signing was the preferred communication method for 18 of the children, 4 preferred to use total communication-a combination of sign and spoken language-and the remaining 7 used spoken language.

Hearing children.

The mean age of the hearing children was 6 years 9 months ( SD = 10.3 months) and ranged from 5 years 4 months to 8 years 7 months. They were all determined to be reading age appropriately (within 6 months of their chronological age), using the BAS II Single Word Reading subtest ( Elliot et al., 1996 ), and were matched to the deaf children on their resulting reading ages from this test. They were predominately matched on a one-to-one basis, apart from five children who could not be matched exactly but were up to 2 months the other side. The large age range of the hearing children was a result of the predominately one-to-one matching criterion. All children achieved a score of at least 85 on a measure of nonverbal intelligence (BAS II Matrices subtest; Elliot et al., 1996).

The hearing children were all pupils in the mainstream schools to which the hearing units were attached to ensure that the deaf and hearing children were matched for demographic variables. There were no significant differences between the deaf and hearing children on reading age, t (58) = 0.25, ns, or nonverbal intelligence, t (44) = 0.56, ns.

Tasks

The children were given two reading tests to measure different components of the reading process: single-word reading ability and sentence comprehension. Measures were also taken of their spelling ability, nonverbal intelligence (NVIQ), phonological awareness, productive vocabulary, short-term memory span, and speechreading ability.

Reading ability.

All children were given two standardized tests of reading; one measuring single-word reading ability (Single Word Reading subtest, BAS II; Elliot et al., 1996 ) and the other measuring word-recognition and sentence comprehension skills ( Primary Reading Test , France, 1981 ). Although the single-word reading test was used to initially match the deaf and hearing children, it was also used, alongside the sentence comprehension test, as a reading outcome measure. Children could respond using their preferred communication method: signing, speech, or a combination of the two. The instructions were given as per the manual but in the child's preferred language. The maximum score for the single-word reading test was 90, and for the sentence comprehension test it was 48.

Spelling ability.

The children were given a spelling-to-picture task, in which they were presented with 30 pictures, one at a time, and asked to write down the name of the picture. The 30 words depicted were concrete nouns, varying in syllabic length (monosyllabic, disyllabic, trisyllabic or quad-syllabic) and regularity (regular or irregular). The words were classified as being regular if there was only one, or a dominant, way of graphemically representing the combination of constituent letters, using phonology-to-spelling feedback consistency (see Ziegler, Stone, & Jacobs, 1997 ).

All words were characterized as being of high frequency (see norms from Kucera & Francis, 1967 ) and early acquired (see age of acquisition norms from Morrison, Chappell, & Ellis, 1997 ) and therefore would be expected to be in most typically developing children's vocabularies. The words were depicted using black and white line drawings taken mainly from Snodgrass and Vanderwart (1980) and were considered to be highly familiar and have low visual complexity (see norms from Cycowicz, Friedman, Rothstein, & Snodgrass, 1997 ). The maximum score was 30.

Productive vocabulary.

This was assessed using the productive vocabulary subtest from the BAS II ( Elliot et al., 1996 ). To answer correctly, children had to produce (using speech, sign, or a combination of both) the correct name for each object, not simply a spoken or signed description. The instructions from the manual were given in the child's preferred language. The maximum possible score was 32.

Phonological awareness task.

This was adapted from Harris and Beech (1998) and measured the child's ability to make a judgment of phonological similarity. Children were shown a brightly colored folder containing 24 pages with three pictures on each. The picture at the top of the page was the item and underneath were a target and a distracter. The first section (consisting of 12 sets of pictures) required the child to make a judgment of alliteration similarity (onset), and the second (12 sets), a judgment of rhyme similarity (rime).

In the rime section, the orthographic congruency of the items was controlled: half the trials contained items and targets that were phonologically and orthographically congruent (e.g., snake-cake ), and the other half contained items that were phonologically and orthographically incongruent (e.g., eye-fl y). On the congruent trials, the children could choose the correct target simply by relying on orthographic cues to judge whether it rhymed with the item and would not necessarily need to use any phonological information. The incongruent trials, however, could only be judged correctly by using phonological knowledge, as the spelling was incongruent.

The words depicted were all characterized as being high-frequency (see norms from Masterson, Stuart, Dixon, & Lovejoy, 2003 ) early age of acquisition words (see norms from Morrison et al., 1997 ). Words in the onset and rime sections were matched for word frequency, age of acquisition, and also on the density of their phonological and orthographic neighborhoods (using database from Masterson et al., 2003 ).

Children were initially pretested on all the pictures. In addition to the test proper, there were two practice trials at the beginning of each section during which children could be given feedback. Children were shown the first practice trial and asked to name the top picture (the item), while the other two pictures were hidden. They were shown the other two pictures and asked to name them and to indicate which of the two bottom pictures began with the same sound as the top picture, for example, bat, bag, man . The same procedure was followed for the rime section, apart from being asked which of the two bottom pictures ended with the same sound as the top picture, for example, fox, bath, box . Once the test proper had begun, no feedback was provided. The maximum score was 24.

Short-term memory span.

This task measured visual, sequential short-term memory span and was developed from the short-term memory span task used in Harris and Moreno (2004) . The task was presented on a laptop computer. The children were shown trials of increasing numbers of pictures to recall in the correct order. The minimum number of pictures presented was two and the maximum was six, with three trials at each list length. The test was progressive and stopped when the child made errors on two or more trials at a given list length. The span was calculated as the maximum number of items that the child could correctly recall on two out of the three trials, and they were given a half point if they recalled one out of the three trials correctly. The task had two sections: the first provided a span of pictures representing words of one syllable in length (e.g., bike, fox, tent, lips ) and the second a span of pictures representing two-syllable words (e.g., apple, flower, rabbit, button ). Short-term memory span was taken as the average over the two word lengths.

The words used in the task were characterized as being of high frequency (see Masterson et al., 2003 ) and acquired early (see Morrison et al., 1997 ). The words were matched on these properties between the two sections. They were depicted by black and white line drawings taken from the Snodgrass and Vanderwart (1980) database. In each trial, the pictures were dissimilar in terms of their phonological, visual, and sign properties. Children were initially pretested on the pictures. They were allowed to give their answers in their preferred language: sign language, spoken English, or a combination of the two. There were three practice trials during which feedback was provided. Once the task proper had begun, no feedback was provided. Presentation of the two sections was counterbalanced. The maximum score was 6.

Speechreading.

The measure of speechreading ability for single words was taken from Harris and Moreno (2006) . Children were shown a video of a woman saying the name of an object (without sound), and they had to point to the corresponding object in a picture array on a board in front of them. There were five different picture boards and 10 trials on each. Children were told that the same pictures could occur twice.

On the first board, the items differed in syllable length and onset and rime (e.g., ball, apple, butterfly ). The second board consisted of two-syllable words that differed in onset and rime (e.g., pencil, dolphin, flower ). On the third board, all the items were monosyllabic and began with the same onset and thus measured the child's ability to differentiate between the endings of words (e.g., ball, box, bus ). The items on boards four and five were also monosyllabic but ended with the same rime thus requiring the child to distinguish between the beginnings of words (board 4: pear, hair, fair; board 5: bee, tree, key ). The rationale for the discriminations required for the different boards came from Geers (1994) .

Children were given the following instructions: "we are going to watch a video. The video is of a lady. The lady will say the name of something on this board (the experimenter pointed to the board) and you have to point to what you think she says. Maybe, the lady will say shoe and then you would point to shoe. But you will not be able to hear what the lady says, you must try and lipread." The instructions were specifically designed so that they could be signed or spoken to mean exactly the same. The order of presentation of the different boards was counterbalanced. The maximum possible score was 50.

Procedure

The children were seen individually on six separate occasions, with each session lasting no more than 20 minutes. Testing took place in a quiet room, usually adjacent to the classroom. For the deaf children, all instructions were delivered using the child's preferred communication method, either signing or spoken English. For the hearing children and orally educated deaf children, all instructions were given using spoken English. All BAS II subtests and the Primary Reading Test were administered as per the instruction manuals.

Results

The experimental tasks were found to be statistically reliable as Cronbach's alpha, for all was above the accepted criterion of 0.7. All data distributions were examined, and where evidence was found of significant skewness or kurtosis, the data were transformed following guidelines from Tabachnick and Fidell (2001) .

Reading and Spelling Achievements

Reading.

The means and standard deviations can be seen in Table 1 . The reading achievements of the deaf children were generally not age appropriate, t (28) = 6.4, p < .001, 95% CI: 8.35-16.20, and there was a mean delay in single-word reading age of 13 months (mean reading age was 6 years 10 months, and mean chronological age was 7 years 10 months). The deaf children's single-word reading delay ranged from 6 months above chronological age to 37 months below. In comparison, due to the selection criteria, the hearing children were reading age appropriately. There were no significant differences between the deaf and hearing children on the measure of sentence comprehension, t (58) = −0.94, ns, 95% CI: −.23 to .08. Both groups appeared to perform relatively poorly on the sentence comprehension test, as only 9 children (4 deaf and 5 hearing) scored highly enough to be given a reading age of 6 years or more on this test, in comparison with the single-word reading test on which 53 children achieved a reading age of 6 years or above.

Spelling.

There were no statistically significant differences between the deaf and hearing children in their overall spelling scores, t (58) = 1.77, ns, 95% CI: −.31 to 5.1, and both groups could spell on average fewer than half the words correctly. The monosyllabic words varied in terms of their sound-to-spelling regularity. A two-way mixed-design analysis of variance (ANOVA; regularity by group) revealed a large regularity effect in that more regular than irregular monosyllabic words were spelt correctly, F (1,58) = 249.51, p < .001, η ² = .43, but no main effect of group, F (1,58) = 2.14, ns, η ² = .017 or significant interaction, F (1,58) = 0.50, ns.

Performance on Language and Cognitive Tasks

Phonological awareness.

The deaf children's accuracy, as a group, was above chance as the mean score was 17.8/24 (74%), and binomial distribution tables revealed that scores over 16.8 were unlikely to have been achieved by chance alone ( p < .05). A two-way ANOVA (group by section) revealed a main effect of group, whereby the hearing children were more accurate than the deaf children, F (1,58) = 19.26, p < .001, η ² = 0.17. There was also a main effect of section, whereby the children achieved higher scores on the items requiring a judgment of onset similarity compared to the items requiring a judgment of rime similarity, F (1,58) = 6.20, p = .016, η ² = .02 but no significant interaction, F (1,58) = 2.27, ns, η ² = .01.

A second two-way mixed-design ANOVA (group by congruency), investigating performance on the rime items for orthographic congruency effects, found a small to medium main effect of congruency, F (1,58) = 18.30, p < .001, η ² = .05, a large main effect of group, F (1,58) = 19.07, p < .001, η ² = .19 but no significant interaction, F (1,58) = 0.10, ns. Therefore, the hearing children achieved higher scores than the deaf children on the rime items, regardless of whether the items were orthographically congruent or not. Additionally, both deaf and hearing children did better when the rime items were orthographically and phonologically congruent.

Productive vocabulary.

The deaf children, as a group, were delayed in their productive vocabulary as the mean age-equivalent vocabulary age was 3 years 7 months, which was significantly lower than their mean chronological age, t (28) = −21.60, p < .001, 95% CI: −55.61 to −45.98, d = 4.0, whereas the hearing children's vocabularies were age -appropriate, t (30) = 0.75, ns. The hearing children had significantly larger productive vocabularies than the deaf children, t (58) = −10.04, p < .001, d = 2.59.

Short-term memory.

A two-way mixed-design ANOVA (hearing status by word length) revealed that there were no statistically significant differences between the deaf and hearing children in their short-term memory spans, F (1,58) = 3.60, ns, η ² = .045. There was no main effect of word length, F (1,58) = 3.92, ns, η ² =.01, although this only just failed to achieve significance ( p = .052). There was also no interaction between group and word length, F (1,58) = 2.16, ns.

Speechreading.

A two-way mixed-design ANOVA (group by board) revealed a medium main effect of group, F (1,55) = 6.73, p = .012, η ² = .07, a medium main effect of board, F (4,220) = 19.51, p < .001, η ² = .09, but no significant interaction, F (4, 220) = 1.52, ns. Thus, although the deaf children achieved significantly higher overall scores than the hearing children on the speechreading task, a similar pattern, across the boards, was exhibited by both groups. Post hoc analysis (Bonferroni t tests) showed that although children scored better on boards 1, 2, and 4, their performance was similar across these three boards. Likewise, although children scored less well on boards 3 and 5, there were no differences between performances on these two boards.

Relationships Between Reading, Spelling, and the Language and Cognitive Tasks

The data were then analyzed using partial correlations and fixed-order multiple regression analyses. The results will be reported separately for the deaf and hearing children.

Deaf children.

Table 2 shows the partial correlations, controlling for the effects of hearing loss, between reading and spelling and the other abilities. Hearing loss was removed from the correlations because the degree of hearing loss exhibited strong negative relations with reading, phonological awareness, vocabulary, and speechreading. As can be seen in Table 2 , both reading tests were strongly correlated with each other and spelling, speechreading, and productive vocabulary. Neither reading test was significantly associated with age, nonverbal intelligence (NVIQ), phonological awareness, or short-term memory span once the effects of hearing loss were controlled. Before hearing loss was statistically controlled, the zero-ordered correlations had shown a significant correlation between both reading tests and phonological awareness.

Spelling ability did not exhibit the same pattern of associations as reading ability, as the only significant correlation was with the two reading tests, and no significant relations were found with any of the other variables. Moreover, spelling ability was not affected by the degree of hearing loss. There were significant small to medium intercorrelations between speechreading, phonological awareness, and vocabulary. Speechreading was also found to have a medium association with NVIQ.

Fixed-ordered multiple regression analyses were conducted to investigate which of the literacy-related tasks explained variance in performance on the two reading and spelling tests. The current sample size of 29 participants is just below the recommended sample size of 31 that is sufficient to detect a large effect with three predictors ( Green, 1991 ), and therefore, the findings need to be interpreted with a note of caution.

As can be seen in Table 3 , when NVIQ and hearing loss were entered in Steps 1 and 2, they explained 27% of the variance in the single-word reading scores. Productive vocabulary was then entered in Step 3, and it accounted for almost 15% of additional variance in performance in the single-word reading ability. When speechreading was entered instead of vocabulary, in Step 3, it explained almost 26% of additional variance. Both speechreading and vocabulary represented a medium effect on the single-word reading scores. A similar pattern was found when the sentence comprehension test was the outcome variable. NVIQ and hearing loss were again entered into the regression analyses at Steps 1 and 2 and explained 41% of the variance in scores. When vocabulary was entered in Step 3, it accounted for an additional 28% of the variance. If speechreading was entered in Step 3 instead, it explained an additional 14% of the variance. As performance on the spelling task did not show any significant associations with any of the other variables apart from the reading tasks, multiple regression analyses were not conducted with spelling as the outcome variable.

Hearing children.

In light of the 3-year age range of the hearing children, age was statistically controlled in all analyses. As can be seen in Table 4 , after the effects of age had been controlled, the only partial correlations that were statistically significant were between single-word reading ability and spelling ability and between spelling ability and the rime phonological awareness scores. Sentence comprehension was not significantly associated with any of the variables 1 .

As performance on the reading tasks did not show any significant associations with any of the other variables apart from age, multiple regression analyses were not conducted with either reading tasks as the outcome variable. Fixed-order multiple regression analyses were carried out to determine which abilities explained performance on the spelling task ( Table 5 ). When spelling was the outcome variable and age was entered in Step 1, it accounted for 52% of the individual differences in the spelling scores. Rime phonological awareness scores were entered into the regression equation in Step 2 and explained a further 8% of additional variance over and above that accounted for by age in the spelling scores.

Differences Between Those Children with Cochlear Implants and Hearing Aids

The data were also examined to see if there were any differences in literacy attainment and performance on the cognitive and language tasks between children with cochlear implants or hearing aids. The children with cochlear implants achieved significantly lower scores on the tests measuring reading ability, spelling, and short-term memory than the hearing-aided children. However, it should be noted that implantation was relatively late in all cases, having occurred at between 3 and 6 years of age. At the time of assessment, the children had only been using their implants from between 1 year 8 months to 4 years 5 months. Taking these factors into account, along with the comparatively small sample size of children with cochlear implants ( n = 7), these results must be interpreted with caution.

Summary of Results

Although the deaf and hearing children's performances were generally qualitatively similar on all tasks, as they exhibited the same experimental effects, there were important quantitative differences between the two groups. The hearing children were more accurate on the phonological awareness and productive vocabulary tasks, whereas the deaf children were more accurate on the speechreading task. The two groups did not differ in their spelling ability or short-term memory span. When the relationships between reading, spelling, and other skills were examined, differences were found between the predictors of literacy in deaf and hearing children although, due to the effects of age, the predictors of reading in hearing children were difficult to determine precisely. For the deaf children, reading and spelling were themselves highly associated yet exhibited different patterns of correlates and predictors. After controlling for degree of hearing loss, productive vocabulary and speechreading (language factors) were significant predictors of reading achievement, but spelling ability was not significantly associated with any of the other variables.

Discussion

The principle aim of the study was to compare two groups of deaf and hearing children, matched for reading ability, on a range of reading, spelling, and literacy-related tasks, in order to examine similarities and differences in their relative levels of performance and in the concurrent correlates and predictors of reading and spelling.

The levels of reading and spelling observed in this study are largely consistent with previous findings in that deaf children, as a group, were significantly delayed in their reading ability (e.g., Allen, 1986 ; Banks, Gray, & Fyfe, 1990 ; DiFrancesca, 1972 ; Harris & Moreno, 2004 ). It cannot be determined whether the deaf children's performance on the spelling task was age appropriate as the test was not standardized; however, as there were no significant differences in the overall spelling abilities between the deaf and younger hearing children in this study (matched on reading level), this could suggest that the deaf children's spelling abilities were delayed for their chronological age although appropriate for their reading level ( Harris & Moreno, 2004 ).

The presence of spelling regularity effects suggests that young deaf children, like hearing children (see Bruck & Waters, 1990 ; Treiman, 1984, 1993 ; Waters, Bruck, & Seidenberg, 1985 ) and older deaf adolescents (see Burden & Campbell, 1994 ; Leybaert & Alegria, 1995 ; Sutcliffe et al., 1999 ), have some knowledge of the relationship between sounds and their corresponding written form. This might reflect either an implicit sensitivity to phonological information or the use of an explicit phonological coding strategy (see Leybaert & Alegria, 1995 ; Sutcliffe et al.). However, as in previous studies, the deaf children showed less detailed phonological knowledge than hearing children of the same reading age as evidenced by their lower performance in the phonological awareness task.

Deaf and hearing children did not differ significantly on short-term memory span, indicating that the deaf children's memory spans were equivalent to those of younger reading age-matched hearing children. This supports earlier findings ( Campbell & Wright, 1990 ; Harris & Moreno, 2004 ; MacSweeney, 1998 ).

There was only one task on which the deaf children outperformed hearing children. This was speechreading. Our finding contradicts previous studies that report equivalent speechreading skills in deaf and hearing adolescents ( Conrad, 1977 ) and children ( Arnold & Kopsel, 1996 ) but supports the findings of Lyxell and Holmberg (2000) . There were some differences between the present study and earlier research. Both Conrad and Arnold and Kopsel contrasted deaf and hearing individuals matched for chronological age, whereas the children were matched for reading level in the current study. However, as the language skills of the hearing children were better than those of the deaf children, it is hard to see how this can account for the difference in the findings. Another factor was the speechreading task itself, which presented video clips of the speaker and used brightly colored pictures. This may have made the task easier for the children and so allowed the superior skills of the deaf children to emerge.

The most significant finding from this study was that the concurrent correlates and predictors of reading and spelling were different for deaf and hearing children. For the deaf children, reading achievement was predicted by the degree of hearing loss, speechreading, and productive vocabulary. However, spelling ability in the deaf children was not associated with any of the other variables apart from reading ability, not even hearing loss. This pattern was consistent with findings from previous correlational studies suggesting that speechreading ( Arnold & Kopsel, 1996 ; Campbell & Wright, 1988 ; Cue, 1996 ; Geers & Moog, 1989 ; Harris & Moreno, 2006 ) and vocabulary knowledge ( Geers & Moog, 1989 ; LaSasso & Davey, 1987 ; Moores & Sweet, 1990 ) could be important for reading ability in deaf children. What the present study established was that both factors appear to be important for reading. In addition, the results suggested a disparity in the relative contributions made by vocabulary and speechreading to the different components of reading: speechreading was the strongest predictor of single-word reading ability, whereas productive vocabulary was the strongest predictor of sentence comprehension ability. The study also points to the need for further research to uncover the spelling strategies that deaf children use because these appear to be different from their reading strategies.

For hearing children of similar age, there is generally a strong relationship between reading and spelling ( Caravolas, Hulme, & Snowling, 2001 ; Jorm, 1981 ; Juel et al., 1986 ; Nation & Hulme, 1997 ; Snowling & Perin, 1982 ) and, more importantly, a similarity in the correlates of reading and spelling ( Bryant, MacLean, & Bradley, 1990 ; Caravolas et al., 2001 ; Ellis & Large, 1988 ; Juel et al., 1986 ). The pattern emerging for the deaf children in this study is thus rather different from what has been observed for hearing children in previous studies. It should be noted in this context that the pattern of predictor variables in the present study was not typical of that found in hearing children because chronological age was the main determinant of reading and spelling ability. However, this is not surprising given that the hearing children had virtually identical reading and chronological ages. Such was the extent of the age effect (prior to it being statistically controlled), that little variance remained to be accounted for. This could explain why even phonological awareness was not found to be a significant predictor, despite it being consistently reported as one of the strongest predictors of reading achievement in hearing children (for reviews see Goswami & Bryant, 1990 ; Wagner & Torgesen, 1987 ). Interestingly, the rime phonological awareness scores did explain an additional 8% of variance, over and above the effects of age, in performance on the spelling task.

Why are productive vocabulary and speechreading important for reading ability in deaf children? Given the specific nature of the vocabulary knowledge that was measured, it appears reasonable to infer that the most plausible explanation lies in the fact that it is easier to read a word that is already in one's vocabulary ( Vellutino, Fletcher, Snowling, & Scanlon, 2004 ). This appears particularly pertinent for reading in a relatively opaque orthography such as English where word-specific knowledge is required to read exception words such as "chaos." An alternative explanation is that good semantic knowledge can provide a compensatory mechanism for children who have poor phonological skills (see Nation & Snowling, 1998 ; Snowling, Gallagher, & Frith, 2003 ). Several authors have argued that deaf children experience such severe delays in reading achievement because they are doubly penalized as they have problems with both top-down and bottom-up processes ( King & Quigley, 1985 ; Marschark & Harris, 1996 ; Webster, 1988 ). However, it may be that those deaf children with relatively good semantic knowledge are not penalized to the same extent and can use this knowledge to compensate in some way for the comparatively poor phonological coding skills usually observed in deaf children. In comparison to the former explanation, which is equally plausible for the relationships observed between vocabulary and single-word reading and vocabulary and sentence comprehension, the latter explanation is more tenable for the stronger predictive relationship found with sentence comprehension where greater levels of vocabulary knowledge could help elicit word meanings from context.

Turning our attention to the strong relationship observed between speechreading skills and reading ability in the regression analyses, we can ask what deaf children might do with the information derived from speechreading, and why would it be important for reading development in a group of children from mixed language backgrounds? Many authors have suggested that speechreading forms the input on which a deaf child's phonological code is based, and indeed, patterns observed in deaf children's spelling errors and rhyme judgments have been found to be consistent with this hypothesis (e.g., Dodd, 1980 ; Dodd & Hermelin, 1977 ; Hanson et al., 1983 ; Leybaert & Alegria, 1995 ; Sutcliffe et al., 1999 ). If speechreading provides such a code for deaf children, is this code similar to the one that hearing children derive from speech, or could it involve a different coding strategy, possibly based upon articulatory gestures?

It has been argued that there is an abstract phonological code, which is not modality specific and which, therefore, could be derived from speech or speechreading ( Alegria, 1996 ; Campbell, 1997 ; Dodd, 1987 ). Indeed, speechread information has been shown to be processed in a similar manner to auditory-presented information ( Campbell & Dodd, 1980 ; Dodd, Hobson, Brasher, & Campbell, 1983 ). An alternative explanation is that, although deaf individuals do use a phonological code, it is qualitatively different to that used by hearing individuals ( Campbell & Wright, 1989 ; Leybaert & Alegria, 1995 ; Lichtenstein, 1985 ) and possibly based upon articulatory contrasts (see Charlier & Leybaert, 2000 ; Harris & Moreno, 2006 ).

It would make sense that the code that deaf children derive from speechreading is based upon the articulatory gestures and motor movements. However, although this might suggest that deaf children develop a different code to that used by hearing children, this is not necessarily the case as it has been argued that speech is not a string of connected sounds but a string of coarticulated abstract phonetic elements that represent articulatory gestures and motor movements ( Byrne & Liberman, 1999 ; Liberman, 1997, 1998 ). On this argument, deaf children develop a code that is fundamentally similar to that derived by hearing children from speech.

One final issue for comment is the significant effect of hearing loss. Many previous studies of reading in severely and profoundly deaf children have not reported a significant effect of hearing loss on reading ( Dyer et al., 2003 ; Harris & Beech, 1998 ; MacSweeney, 1998 ; Waters & Doehring, 1990 ). However, two studies investigating reading achievement only in profoundly deaf individuals have observed such a relationship ( Geers & Moog, 1989 ; Moores & Sweet, 1990 ). One possible explanation for the significance of the effect in our study is that, with the advent of better hearing aid technology, children with lower losses are at a greater advantage than those with higher losses because residual hearing is used more effectively. As noted in the introduction, previous research has been equivocal about the importance of the relationship between reading and phonological awareness in young deaf children although stronger relationships have been observed in older deaf adolescents ( Campbell & Wright, 1988 ; Dyer et al., 2003 ). It is not clear whether any of the previous studies, which reported significant associations between phonological awareness and reading, controlled for hearing loss. If they did not, it is possible that the relationship between phonological awareness and reading could be mediated by hearing loss because in our study phonological awareness was only a significant correlate of reading ability if hearing loss was not controlled.

In conclusion, we have shown that both speechreading and productive vocabulary are important skills for reading achievement in deaf children from varied language backgrounds. Spelling was not related to any of the measures in the present study, apart from reading, suggesting that, although reading and spelling ability are related in deaf children, they are also fairly disparate skills as they exhibit different patterns of correlations. Longitudinal studies need to be conducted in order to determine the importance of speechreading and productive vocabulary knowledge for reading development and to investigate the apparent disparity between reading and spelling strategies, in deaf children.

This research was conducted as part of doctoral work of F.E.K. at Royal Holloway, University of London. F.E.K. was supported in the preparation of this article by Economic and Social Research Council Postdoctoral Fellowship PTA026270527. The authors thank all the children and their teachers who took part in this study.

References

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Concurrent Correlates and Predictors of Reading and Spelling Achievement in Deaf and Hearing School Children | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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In contrast to the converging findings from hearing children, fairly equivocal findings have emerged from similar studies of reading in deaf children. Although there have been many studies looking at which skills are associated with reading ability, few have considered more than one ability in the same study, and so they do not show the relative contributions of each skill.

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What really matters

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Abstract

Earlier identification of hearing loss allows for earlier intervention and raises expectations that increasing numbers of deaf children will develop language and literacy abilities that are comparable to their hearing age peers. 1 By implication, such expectations focus attention on what happens in the early years of literacy learning as these experiences have been shown to be critical to future success for hearing children. It is during these years that the groundwork is laid for understanding the functions of text and the strategies that can be employed to make sense of print, including the principles of how an alphabetic writing system works. "Emergent literacy at school entry may be viewed as particularly important because of its association with later reading [and writing] skills and the importance of these abilities for school success generally" ( Barnett, 2001, p. 421). There would be no reason to imagine that these abilities are any less important in the case of children who are deaf.

"A robust body of knowledge exists about the first five years of life and the extent to which children's early experiences correlate with their competencies in language and literacy" (Ramey & Ramey, 2006, p. 445). With respect to hearing children, much has been written as to the nature of the experiences and interventions that support optimal early literacy development, especially for those learners who are at particular risk of having difficulty developing literacy skills. By virtue of their hearing loss, deaf children fall into this at-risk group, and given the renewed emphasis on the importance of early intervention, it is timely that we revisit our understandings of the nature of early literacy development for these learners.

Suggestions have been made that, with respect to early literacy development, 2 deaf children follow similar trajectories to those of their hearing counterparts. In a review of the literature, Williams (2004) writes that "deaf children's emergent reading reflected the developmental sequence of hearing children described in the research literature" (p. 356) and that "young deaf children's emergent writing development may be similar to that of hearing children" (p. 361).

Given these indications of a parallel start, it would seem reasonable to expect that most deaf children would go on to develop text-based literacy abilities commensurate with their hearing age peers. Yet it remains the case that 50% of deaf students graduate from secondary school with a fourth grade reading level or less ( Traxler, 2000), and 30% leave school functionally illiterate ( Marschark, Lang, & Albertini, 2002). This begs the question as to when the language-learning trajectories of deaf children begin to draw apart from hearing learners to the extent that outcomes are so divergent, suggesting that perhaps the early literacy development of these two groups is less similar than it appears on the surface. Are there aspects of development that are playing out differently between the two groups that we have failed to take into account? Are these the aspects that are critical to future success in learning to read and write? Are we missing what really matters in the early literacy development of deaf and hard-of-hearing (D/HH) children?

Language and Early Literacy Development

For deaf children, the import of this language-early literacy connection can have implications in two ways. First, many deaf children have delays in their face-to-face language development which can negatively affect literacy learning. "The frequently reported low literacy levels among students with severe to profound hearing impairment are, in part, due to the discrepancy between their incomplete spoken language system and the demands of reading a speech-based system" (Geers, 2006, p. 244). Second, deaf children whose first language is not English (e.g., ASL or some other spoken or signed language) are faced with developing literacy in a language they may not have yet acquired.

Mayer and Wells (1996) provide a framework (see Table 1) for considering the relationship of language and literacy in the development of both hearing and deaf learners that can be used as a model for considering how early literacy is positioned with respect to the development of face-to-face language (spoken, signed, or some combination) and the subsequent development of reading and writing.

Mayer and Wells (1996) outline four overlapping phases in the process of becoming literate, with progress through the phases depending on having a linguistic bridge or means to mediate development in and between phases and on having access to a language-learning situation that meets a particular set of conditions. These conditions are (a) adequate exposure in quality and quantity, (b) to accessible linguistic input, (c) in meaningful interactions, (d) with others who are already capable users of the language. Although there is certainly variability with respect to the relative quality of these linguistic interactions for all learners ( Wells, 1986), it can be generally assumed that for most hearing children the minimal conditions for language acquisition are being met.

The first phase in the process is concerned with the development of language for face-to-face communication. Given the above conditions are in place, language acquisition at this stage happens relatively effortlessly as children use language as a tool to mediate interactions in their environment. In this way, the means to acquiring the language is the use of the language itself ( Halliday, 1975), and this can happen just as effectively in a spoken or a natural signed language-or perhaps, in the signed form of a spoken language ( Luetke-Stahlman, 1998).

However it is worth noting that, unlike the situation for most hearing children, for deaf children there are usually challenges to be addressed with respect to meeting at least one of the necessary language-learning conditions (e.g., making the input accessible via amplification or signed language). The result is that many deaf children may not have acquired a face-to-face form of their first language in the unfettered way that their hearing counterparts have. Given improvements in amplification technology (including cochlear implants) and possibilities for more timely educational interventions, one of the anticipated benefits of earlier identification of hearing loss is that deaf children will have increased probabilities of developing age-appropriate language skills. Yet despite advances in many areas, there continue to be concerns in this regard (see the discussion in Paatsch, Blaney, Sarant, & Bow, 2006).

Without a full face-to-face language in place, deaf children often do not have the requisite basis in place for age-appropriate cognitive and literacy development. There is a need to emphasize this point as suggestions have been made that face-to-face language (spoken or signed) is not a key element in the early literacy development of deaf children and that even in the absence of much fluency in this area, deaf children are able to make "gains in literacy knowledge comparable to those made by hearing children" (Rottenberg & Searfoss, 1992, p. 477). Although it is certainly true that print can and should be meaningfully introduced to children at a very young age, it is not the case that, for hearing children, this exposure to print occurs in absence or lieu of concomitant spoken language development.

There is no reason to believe that deaf children are unique in this respect. Dyson (2001), in making reference to Ramsey (1997), contends that "it is difficult to see how children could learn to compose with written graphics unless they could already use comfortably a natural language (spoken or signed) as a tool to plan, narrate, make queries and even reflect on, and analytically examine speech itself" (p. 128). Thus, it can be argued that the first aspect of what matters in early literacy development is that children have near to age-appropriate spoken and/or signed language fluency in place. All subsequent claims made in this paper with respect to early literacy learning are predicated on this premise.

Phase 3 is pivotal to this discussion of early literacy development as it is at this point that children are asked to express themselves, not only in their face-to-face to language but also in print. Things that have heretofore been spoken, signed, or thought about must now be committed to paper. This can be a daunting task as children try to make sense of the relationships between the language they already know and the language of print, encountering "rich conflicts" along the way ( Grossi, 1990). Learning of reading and writing, although not children's first attempts at making representations, does constitute "their first encounter with what will appear to them as arbitrarily constructed, unmotivated signs" ( Kress, 1994, p. 219).

To accomplish this task, hearing children exploit knowledge of their face-to-face language as they talk their way into text. "Children are highly proficient in all aspects of the syntax of speech at this stage. That proficiency provides the linguistic foundation on which they build when they first learn to [read and] write" ( Kress, 1994, p. 53). This is the point in the literacy-learning process when the commonalities between speech (sign) and print are more important than the asymmetries, as children rely on these commonalities to decode and encode print ( Perfetti, 1987).

The questions that arise when thinking about the early literacy development of deaf children rest on how these learners make sense of print and how it is that they talk or sign their way into text. How do they resolve the rich conflicts that arise when they sort out the relationships between their face-to-face language and text? What are the necessary understandings about print that must be established at this stage if age-appropriate literacy is to be achieved? The focus for the remainder of this paper will be on a detailed examination of this third phase of development as it is central to the question of what matters for deaf children in their early literacy development.

However, before moving on to this examination, it would be important to make note of what occurs in Phase 4 as this is the level that is concerned with the development of literacy for educational purposes. At this stage, it is assumed that basic literacy has been established and that the connection between face-to-face language and print has been made. This stage is typified by more complex uses of text and the use of the synoptic genre. It is in the synoptic written genres (e.g., expository texts such as essays, arguments, etc.) that discipline-based knowledge is typically constructed and communicated, typified by the use of low-frequency vocabulary, compound-complex grammatical constructions, and grammatical metaphor (Halliday, 1993). Fluency at this stage goes far beyond a functional level of literacy (i.e., Grade 6 level), is necessary for advanced academic study, and is the standard by which success as a literacy learner is often measured-a standard that many deaf learners often fail to meet.

Stages of Early Literacy Development

As is characteristic of many discussions of early literacy development in hearing children, writing, rather than reading, will be used as the basis for the analysis. More specifically, written samples will be used as the means for comparing young deaf and hearing writers in order to consider the texts of deaf children in relation to what is typically seen in the development of hearing children and to illustrate the features of development at each stage. It needs to be clarified here that the purpose in using these examples is to provide a fulcrum for a theoretical discussion of what is necessary for the development of age-appropriate literacy. The specific details of the two studies from which the samples are taken have been reported elsewhere ( Mayer, 1998; New Zealand Ministry of Education, 2005) and will not be repeated here. That said, it is still necessary to provide sufficient background information about the children in these two studies to provide a basis for the interpretation of the samples and a sense as to the extent to which the claims made might be generalizable to the broader population of deaf children.

A total of 115 deaf children were involved in the two studies. However, given the focus on early literacy, only samples from the 30 writers between the ages of 4 and 7 years were considered for this paper as this represents the group who would be typically identified as early literacy learners. All these children attended public schools (in a school for the deaf or in a mainstreamed setting) and used sign or some combination of speech and sign as their primary means of communication. As is too often the case, a number of these children did not have a firmly established first language (signed or spoken) upon school entry. With few exceptions, the children had profound hearing losses and regularly used some form of personal and/or group amplification. The extent to which this amplification provided access to spoken English varied among the children, and given the fact that they all used sign to communicate, it could be argued that as a group they did not have full access to language via audition alone. It would also be fair to say that the level of signed language proficiency varied from child to child with some relying more on a natural signed language (ASL or New Zealand Sign Language) and others on some form of English-based sign.

With respect to generalizability, it is also reasonable to suggest that this group of young literacy learners is representative of deaf children in similar contexts as the nature of their written products is similar to those reported in other studies of early literacy development (e.g., Andrews & Gonzales, 1991; Ewoldt, 1985; Ruiz, 1995; Schleper, 1992). And even though all these young writers use sign as an aspect of their communication, what we learn from them has relevance for thinking about early literacy development in oral deaf children as well, as the key issue is language not modality. What is also telling is that even though there is considerable group variation with respect to the nature of their early experiences and cognitive and linguistic aptitudes, there is very little difference among the writers in their early efforts to create English text. This seems to indicate that there are other common underlying issues that may help to explain why such a diverse group of deaf children produce texts that are so much alike.

In the following sections, three levels of early written language development are presented, in which the products of hearing and deaf writers of like ages are juxtaposed in order to draw attention to the similarities and differences among them at each stage. These examples are intended to be illustrative of what is (or should be) happening at each stage and to indicate differences between young hearing and deaf writers that could help inform our understanding of early literacy development in deaf children. This premise is driven by the fact that, in the case of hearing children, the sequence of early written language development is well documented ( Tolchinsky, 2006), with any divergence from this pattern indicating potential difficulties with future literacy learning ( Clay, 2002; Cramer, 2006). Looking at the development of deaf writers against this backdrop could help illuminate how departures from expected patterns may matter in terms of later literacy development.

Level 1: Distinguishing Writing From Drawing

In Level 1, children search for criteria to make the distinctions between the visual representations of drawing and writing. The key understanding developed during this level is the notion that, although the same kinds of lines are used in both drawing and writing, the lines function differently in terms of what they are meant to represent. "When we draw, the lines are organized following the object's contours; when we write, the same lines do not follow the object's contours. When writing, we are outside the iconic domain" (Ferreiro, 1990, p. 15). Drawings look like the objects they are meant to represent, whereas writing does not (i.e., the picture of the house vs. the word "house").

In their first attempts at writing (vs. drawing), children essentially draw a picture of the text, making it look like the examples of texts they have seen. Although the two products may appear indistinguishable to the viewer, children can differentiate between them so that even though both representations may look scribbles, the child will identify one as the picture and the other as the text (Harste, Woodward, & Burke, 1984). With active opportunities to explore text, writing begins to assume an even more text-like form (e.g., scribbles that move from left to right, parallel rows of scribbles, spaces between scribbles), and children begin to incorporate standard letters into their writing in a random fashion. This is a consequence of exposure to print in the environment and a growing awareness that letters are a regular feature of text.

Children assign meaning to texts at this level, and they understand that written language is a form of communication. But because representations are not standard, the meaning cannot be reconstructed from the text without the assistance of the author. As the text itself does not drive the retelling, the reading of the text may change from one incarnation to the next. Overall this first level in children's thinking produces two major accomplishments: "(1) to consider strings of letters as substitute objects, and (2) to make a clear distinction between two modes of representation-the iconic mode (to draw) and the noniconic mode (to write)" (Ferreiro, 1990, p. 16).

An analysis of texts at this level reveals very little difference between the writing of hearing and deaf children, and both appear to have achieved the understandings fundamental to this stage. The samples in Figure 1a and 1b were created by a 4-year-old hearing student and a 5-year-old deaf student, respectively. Both examples feature a clear distinction between picture and text, with writing that consists of scribbles in spaced, linear rows, and it was not difficult to find numerous similar samples from both groups in the data set. Both hearing and deaf children are able to attach a meaning to the text and are able to relate this meaning via their face-to-face language. They both have a clear sense of what they want to write about, and they recognize that the text can carry this meaning. They are using a text-like form to create this written message, but there is no apparent relationship between the written and spoken/signed mode. Any rereading of the story is not bound by the constraints of the text, and the reading may change from one instance to the next.

Level 2: Identifying Properties of Writing

Development in the second level is typified by an increasing understanding of the properties of text, with the recognition that there are differentiations between words. Children become aware of quantitative and qualitative principles for creating words (Ferriero, 1990; Perez, 2004), developing the sense that there are minimum or maximum numbers of letters in a word and that certain variations within a word are improbable (e.g., four consonants in a row, a word with no vowels). They also come to see that identical letter strings represent the same object. Coming to these understandings is a consequence of continued exposure and interaction with text, and children's writing will evidence the features of the writing system to which they are exposed. In the case of English, this means the use of the standard alphabet and the incorporation of some memorized patterns (e.g., child's name, high-frequency vocabulary).

It is also at this stage that children may begin to encode characteristics of objects into the written word, coming to the conclusion that "train" must be a long word because a train is a long vehicle ( Papandropoulou & Sinclair, 1974). Olson (1994) gives the example of preschool children who, when asked to write "cat," will write a short string of letters and then, if asked to write "three cats," will repeat the same string three times. In this way, the text produced is a representation that is emblematic of the cats themselves, rather than of the words "three cats."

What marks this stage is that, just as in the previous level, it precedes any knowledge of the relationship between the sound patterns of the word and the written representation. The young writer is making a direct relationship between the object and the text. The construction of the text hinges on visual rather than auditory principles, and even though the child understands that the text conveys the meaning of the spoken (or signed) utterance, there is no explicit understanding as to how one representation is captured in the other. The child is using the text as a sort of iconic representation of the object itself, rather than as a representation of the word (or sign) for that object. A similar manifestation of this understanding is when prereaders "read" logos such as "McDonald's" and "Coke," interpreting these items as standing directly for the things themselves, not for the words they represent (Olson, 1994).

Just as in Level 1, there is little to differentiate the texts of the deaf and hearing writers. In fact, it is often the case that deaf children will be more effective at using standard letters and overlearned words in their writing as, by virtue of structured early language teaching, they have often had more systematic instruction in these areas than the typical hearing child. The samples in Figure 2a (created by a 5-year-old hearing student) and Figure 2b (created by a 6-year-old deaf child) exhibit the features that are typical of development at this stage.

Both writers use combinations of vowels and consonants in creating words and include a few words whose spellings have been memorized (e.g., the, is, see). They apply principles of quantity and quality, with words of reasonable length (i.e., at least three letters and no more than nine), with a different set of letter combinations to represent different meanings (i.e., the same set of letters cannot mean the same thing). They organize the writing in a text-like fashion and are readily able to provide a gloss for what they have written.

Level 3: Connecting Writing to Spoken/Signed Language

This third level, central to the move from emergent to conventional literacy and crucial to any discussion of early literacy development, is the stage at which the writing of many deaf children begins to look markedly different from that of their hearing peers. In referring to all language learners at this level, Teale (2003) suggests that "understanding this transition is especially critical because a significant number of children get hung up in their learning during this time, and because what happens during this period seems to have significant influence on children's progress in literacy achievement across later grade levels" (p. 26).

It at this juncture that hearing children begin to make use of alphabetic principles or what Ferriero (1990) calls the "phonetization of the written representation" (p. 20). She describes this all important shift as one in which children "learn that letters serve the function of representing that fundamental property of objects that drawing is not able to achieve, that is, their names" ( Ferreiro, 1986, p. 28). In other words, this is the stage at which children come to see that, rather than a direct relationship, there is an intermediary step between writing and object that is realized in the words (signs) of the face-to-face language. Thus, the challenge of learning to read and write is to find or detect aspects of one's own implicit linguistic structure that can map onto or be represented by the script ( Olson, 1994). In this process, children bring together two sets of understandings-the knowledge of their face-to-face language and what they have come to know about how print works. To do this they must have access to the structures of their own speech (or sign), to provide the data for reflecting on language, and they must also have access to a set of conventional print categories (e.g., letters, written words) into which the data can be organized ( Homer & Olson, 1999). In the normal course of events, the two sets of data converge as there is a culturally predetermined way in which any language is represented in its script. The task for young writers is to uncover this relationship.

Invented spellings are typical of hearing children's work at this stage and provide evidence of how young writers are beginning to make the connection between spoken and written language. In the process of inventing spellings, hearing children exploit sound-symbol correspondences in order to make sense of the connections between speech and text as they work to commit language to paper ( Kress, 1997). Readers, familiar with the language, are able to decode these texts using their knowledge of the language's sound-symbol correspondences. Note the spellings of "ons abon a tim" for "once upon a time" ( Figure 3a) and "aotobyografe" for "autobiographies" ( Figure 3b) in the texts of two young hearing writers.

It has also been found that, for hearing children, the use of invented spelling, which is phonologically rather than orthographically driven, is a strong predictor of later progress in decoding and understanding the alphabetic principle (for a discussion, see Whitehurst & Lonigan, 2001). In inventing these spellings, children use the point of articulation as a reference for making sound-symbol decisions, and they can often be seen to exaggerate articulations in an attempt to get a feel for the sounds they are attempting to write ( Juel, 2006). It is in this process that hearing children draw on their knowledge of letter names as they write, to aid in making sense of grapheme-phoneme associations. With the exception of "W," letter names contain some of the relevant phonemes that are symbolized by that letter (e.g., tee contains /t/, and eff contains /f/; Ehri & Roberts, 2006), and therefore, it is reasonable that young writers would make use of this relationship.

Studies have shown that deaf students can and do invent spellings and are analytical and logical in the process ( Ewoldt, 1985; Mayer, 1998; Schleper, 1992). As do hearing children, they understand that there is a relationship to be made between speech (or sign) and text. They employ strategies such as mapping handshapes onto English words, using lip patterns as cues to the beginning sounds of words, and linking finger spelling to text. These strategies are evident in the text in Figure 3c that was written by Jane, a 6-year-old deaf child of hearing parents who communicated, depending on the interlocutor, through either ASL or a combination of speech and sign. Her writing is representative of the performance of the majority of deaf students in this data set and not unlike the texts described in other studies of deaf children's early writing.

While Jane is still evidencing features of the previous level in this example (i.e., use of memorized, high-frequency words), she is also demonstrating an awareness of the need to make a sign-print connection that was not evident in her earlier work. Jane exploits the fact that some signs are produced with handshapes that can also be seen as letters from the manual alphabet, and she comes to the conclusion that this handshape represents the first letter of the word for that sign (e.g., in ASL, the sign for "onion" is made with an "x" handshape, therefore, onion starts with "x").4 This notion is reinforced for her by the fact that many name signs are produced by using the letter from the manual alphabet that is the first letter for that name (e.g., "Nancy" is signed with an "n" handshape).

Thus, to invent spellings, Jane relies on the initial handshape principle as the rationale for determining the first letter of a word (Mayer, 1998). Once this has been established, she simply adds a set of random letters to stand in for the rest of the word as there is no other apparent connection to be made between the sign and the text. For example, in the second line of her text, Jane writes the invented word "kissb" to mean "punk." Because she signed the word "punk" using a "k" handshape moving from the forehead across the top of the head, it made sense to her to begin the word with a "k." She completes the word with "issb." It could be the case that she chose "kiss" because it is a learned pattern, but the key point is that, with the exception of the first letter, her invented spelling does not map onto "punk" in any systematic way.

In the next line, she writes "crases" to mean "long hair." The "C" handshape of the classifier she used to sign "long hair in a ponytail" becomes the first letter of her invented spelling for the word. In the fourth line of the text, she is able to write the correct first letter for the color words (e.g., "gous" for "green") as these initial letters are evident in the production of the sign (i.e., the "g" handshape is used to sign "green").

In relying on her knowledge of the relationship between handshapes and the manual alphabet, Jane is able to make an orthographic link to English print as she sorts out the conflicts between sign and text. But this sort of letter knowledge is qualitatively different from the letter naming that hearing children rely on as they invent spellings, and as a consequence, she produces a markedly different written product.

The distinction between Jane's efforts and those of her hearing peers is that it is not possible for a reader with knowledge of the language to decode her writing. There is no way to sound out the meaning of individual words because the representations are not constructed on this basis. Although Jane attempts to exploit commonalities between her language and the text, her efforts do not yield representations that are close enough to standard that they can be read. As well, beyond the lexical level, she is unable to capture the syntax of her face-to-face language in the text in any systematic way or in a way that conveys the meaning of the signed (or signed and spoken) utterance. This is not surprising as the face-to-face language that she is encoding is not English-yet that is the language she is attempting to write. Although she knows what the text means, when reading back what she has written, she is unable to make a consistent link between "talk" and text, and she "tells" rather than reads backs what she has written.

In the data set, there was one significant exception to this pattern. This was the text written by 6-year-old Kate (see Figure 3d)-a text that exhibits features of the writing of both her hearing and deaf peers. As was the case for all the children, Kate used her face-to-face language to drive creation of the text, in her case, a combination of mouthing and English-based sign. She was not a candidate for amplification, her speech was unintelligible, and it would be accurate to say that her first language was a form of manually coded English as this was the form of communication that she was exposed to by her hearing parents. Her face-to-face English was age appropriate, and when she communicated, the English was represented almost completely via mouthing and signs, although she did not always represent every word in both modes. Like Jane, her invented spellings are not phonetically based but are derived from the relationship between handshape and manual alphabet. For example, because she uses an initialized sign for "cottage," she begins her invented spelling with a "c," and as Jane did, she uses a random set of letters to complete the word, spelling it as "chyeng." And just as in Jane's case, it is not possible for a reader of English to independently decode the text.

But unlike Jane, Kate was able to read back her writing in correct English word order with a one-to-one match between sign/mouthing and text, including all the function words typically omitted by deaf students. Notice the direct correspondence at the word level between the gloss and the text in Figure 3d. Therefore, although idiosyncratic with respect to spelling, Kate achieves the critical milestone of the third stage of literacy development. She represents English at the morphosyntactic level, making a one-to-one link between face-to-face language and print, mouthing, and signing her way into text.

What is ultimately most significant about Kate's development is that she is the only student among this group who went on to develop age-appropriate text-based literacy abilities. Although both Jane and Kate (along with all the other children in both studies) achieve conventional spelling, it is only Kate who ultimately produces a standard English text (see Figure 4a and 4b). This suggests two things-that Kate has a command of face-to-face English (even in the absence of intelligible speech) and that she employs effective strategies in committing that language to paper. Yet, given the nature of her invented spellings, it is clear that in some ways these strategies are unique to her as a deaf literacy learner. Developing a better understanding of the singular ways in which Kate and other successful deaf literacy learners master this third level of development seems key to developing a fuller understanding of early reading and writing processes in these children.

The Challenge of Representing Face-to-Face Language in Text

What is striking across all levels of early literacy development described here is that deaf children, like their hearing peers, are active theory builders and constructors of knowledge ( Clay, 1983; Kress, 1997). In early writing activities, they are motivated to make sense of text in terms of their present theories of how language works, operating on problems and finding their own solutions. For all young learners at this early stage, the point of engagement with text is not that they always come to conventional solutions for all the conflicts they face in committing language to paper, but that they are able to identify problems and attempt to solve them in systematic and logical ways. It is heartening to see, and a testament to the cognitive capacity and flexibility of deaf children, that they do so if provided with the appropriate opportunities (e.g., see Andrews & Gonzales, 1991; Ewoldt, 1985; Mayer, 1998; Ruiz, 1995; Williams, 1999). In all these studies, instructional programs were designed to allow children to exploit possibilities for constructing meaning that are generally afforded to hearing children (e.g., interactive storybook reading, writing workshop). In many respects, the deaf children in each case were able to take advantage of these learning opportunities in the same way as their hearing counterparts (e.g., responding appropriately to text, developing concepts of print) or, if not in the same ways, in ways that demonstrated that they understood the meaning-making problem (e.g., attempting invented spellings). Given this evidence, therefore, it appears that the problems of early literacy learning do not rest on the fact that young deaf children are unable to act as constructors of knowledge, and on this aspect at least, there is a striking similarity to young hearing learners.

It could also reasonably be argued that with respect to the first two levels of early literacy development, young hearing and deaf writers are on parallel, comparable trajectories in terms of learning to write. The similarities evident at this stage may go some way to explaining why researchers in the field have made a case for symmetry between the two groups (Williams, 2004). However, what needs to be remembered is that at these early stages writers are not yet making explicit connections between face-to-face language and text. Young children are definitely aware that print conveys meaning and that as writers and readers they can construct and reconstruct this meaning, but they still see text as a direct representation of the object to which it refers. Making this direct relationship depends to a great extent on the visual mode (i.e., what text looks like), a channel in which deaf learners are unimpeded and in which they may even have a slight advantage. Thus, it is not really surprising to find that, at the earliest stages of literacy learning, deaf children's written products look much like those of their hearing peers. It could even be argued that the underlying strategies for constructing written representations are similar between the two groups.

Yet irrespective of writing system, children must come to see the systematic relationships between their face-to-face language and the text and then reconsider their language in terms of this relationship. Such an interpretation is in line with the view of Ferreiro and Teberosky (1982) that, for hearing children, written language learning is a relearning of spoken language and with Watson's (2001) contention that "any orthography necessarily creates conceptual categories for thinking about language in that it requires the user to segment the stream of speech into units that can be described by that orthography. Orthographies can be thought of as the theories of language they are created or adapted to represent" (p. 44).

This is the major accomplishment of the third level of early literacy development, and it is at this stage that the trajectories of hearing and deaf children begin to diverge. It is problems in the evolution of this relationship that lie at the heart of the challenges that face deaf children in the development of reading and writing. As has been argued via the examples presented, deaf children have a sense of the task and attempt to make relationships between language and text, but it appears that they often lack the necessary knowledge and strategies to do so effectively. The challenge for educators and researchers is to acknowledge and identify what is lacking and then to think about ways in which these gaps can be addressed.

With respect to these gaps, two main points should be emphasized. The first is the lack of knowledge of the face-to-face language to be written. This does not mean the absence of any face-to-face language, but rather an inadequate or incomplete foundation in the language to be written. Learning to read and write, even in a perfect alphabetic writing system, "would still depend on having the oral language with which to understand the morphemes and it would still depend on being able to connect letter sequences to their pronunciation .... Sounding out is based on the assumption that children know the meanings of the words they are decoding. Phonics works only if the string of produced letter sounds approximates a recognizable word" ( Juel, 2006, p. 423). Even for hearing children who are learning English as a second language, the basic argument is that it is very difficult to learn to read and write a language that is unfamiliar in its meaning and sounds and that [for literacy development] there needs to be at least minimal familiarity with the target language, although how familiar is unclear ( Foorman, Goldenberg, Carlson, Saunders, & Pollard-Durodola, 2004). This raises questions as to what level of English fluency (in speech and/or sign) is necessary as a precursor or concomitant condition to help ensure that deaf children will be successful at learning to read and write.

For all beginning literacy learners, written text and face-to-face language must come to make sense in terms of each other, constituting a symbiotic relationship that young children need to unravel ( Homer & Olson, 1999; Olson, 1994). Hearing children do this by using the structures of their speech as the fodder for making sense of text, and in turn, the text provides a model for rethinking the nature of the spoken language (e.g., the concepts of word and letter).

Consider the oft-used English example of "elemeno." As a consequence of numerous recitations of the alphabet (and more especially the alphabet song), hearing children come to believe that "elemeno" is a word. It is only after exposure to the alphabet in its printed form that they come to realize that "l," "m," "n," and "o" represent individual letters, not a word. This provides a simple but instructive example of the ways in which print brings spoken language into consciousness, thus helping the young reader and writer make the connections between the two.

The second point, which is intimately tied to the first, is the inability of many deaf children to effectively employ the range of strategies used by hearing children in making the relationships between face-to-face language and text. Whitehurst and Lonigan (1998, 2001) propose that these are two interdependent sets of processes and skills-"outside in" and "inside out". In contrasting the two, Pressley (2006) describes "outside in" components as developing through informal social processes in the preschool years and coming from outside the printed word. These include general language competencies such as knowledge of concepts, words, syntax, story structure, and the conventions of print. "Inside out" components are more specific to text and derive from sources of information within the printed word. They allow for the rendering of sounds into letters and include knowing the names of letters and letter-sound associations and phonological awareness. "Outside in" skills support general comprehension and are more important somewhat later in the literacy-learning process, whereas "inside out" skills are seen to be vital in the early sequence of learning to read and write when the emphasis is on making sense of the linguistic code itself ( Lonigan, 2006).

The most debated of these "inside out" skills is phonological awareness. Phonological awareness "refers to the ability to detect or manipulate the sound structure of oral language" ( Lonigan, 2006, p. 78). 5 With respect to hearing students, it is the primary means by which they make the systematic connection between talk and text, and the only variable which has shown a causal, rather than a correlational, relationship in the development of literacy ( Phillips & Torgensen, 2006; Pressley, 2006; Scarborough, 2001). The case for its importance for young hearing literacy learners is overwhelming, making it a better predictor of early literacy success than any other variable including IQ ( Stanovich, 2000). "Although phonological recoding may play a minor role in skilled adult reading, it plays a critical role in helping the child become a skilled reader, as it is the principal mechanism by which beginning readers (and writers) learn to use the more efficient visual route and achieve competent performance ( Form & Share, 1983, p. 114). Arguments have also been made that the development of reading depends on phonological awareness in all languages studied to date, even though developmental differences may be identified given that languages vary in the ways in which phonology is represented in orthography ( Ziegler & Goswami, 2006).

Although the focus of this paper is not to summarize the debate as to the relative importance of phonological awareness in early literacy development, it is important to acknowledge contentions that its role in learning to read and write has been overstated. For example, in a letter to the editor, Krashen (2002) questions "the necessity of phonemic awareness training for English speaking children" (p. 128), basing his position on reviews of the research in the area ( Krashen, 1999, 2001). In response, Ehri, Shanahan, and Nunes (2002) suggest that "Krashen has relied on the statistical significance of individual studies in making his case but has ignored the issue of power and the contribution of meta-analysis" (p. 128). Further to this point, Shanahan (2004) suggests that although Krashen and others (see Coles, 2001; Garan, 2001) may (or may not) be correct in what they say about particular studies of phonemic awareness, their overall research synthesis is flawed as they do not take an even-handed approach in accounting for studies that both challenge and support their position.

With respect to deaf children, numerous arguments have been made that phonological awareness does play a role in the reading process ( Burden & Campbell, 1994; Hanson, 1986, 1991; Hanson & Fowler, 1987; Kelly, 1993; Leybaert, 1993; Waters & Doehring, 1990) and in skilled reading ( Hanson, 1989; Kelly, 1995). Other studies (e.g., Miller 2002, 2006) have indicated that deaf individuals, despite poor phonological awareness, have word-processing skills comparable to those of hearing learners. But given that the reading levels of the deaf learners in these studies are substantially below those of their age-matched hearing peers, there is a question as to how much this ability impacts on actual reading levels. Miller (2002) speculates that although "this apparent equality at the word level is encouraging, it may simply imply that phonological processing contributes to later and higher stages of text processing and not, as examined in this experiment, to the initial encoding of words" (p. 325).

In any case, it is not phonological awareness per se, but what it affords, that makes it so crucial in the development of early literacy. It is the primary means by which hearing readers and writers come to make the systematic connections between spoken language and text and to sort out how print works. Even though there may not always be a one-to-one match between sound and symbol, it is the strategy that is most effective in allowing writers think about their language in terms of the text, holding true in every language studied to date, even Chinese (Ashby & Rayner, 2006).

It is also informative that even when hearing children have the requisite spoken language in place, they can encounter difficulties in reading and writing, and in the majority of cases, these are attributable to deficits in phonological awareness ( Scarborough, 2001; Stanovich, 2000). This suggests that it may not be a question of whether, but how, phonological processing skills are taught. Yet with respect to the early literacy learning of deaf students, it has been suggested that "the 43 or 44 phonemes of spoken American English present a barrier to reading, when our print system has only 26 symbols and that there is a closer relation between print and the manual alphabet, thus enabling us [deaf learners] to bypass phonology" ( Moores, 2001, p. 3). Yet there is no evidence to indicate that young deaf children can become successful readers and writers by bypassing phonology, entirely circumventing the set of strategies that have been found to be critical for every other group of English language learners. Therefore, rather than imagining that we can bypass phonology, it would be productive to redirect our attention to the ways in which we can help children to solve the phonologic problem of spoken language in ways that make sense for children who are deaf.

Implications for Educational Research and Practice

If we want to improve outcomes, it is key that we focus our attention on those areas that have proven to be most problematic for deaf children (e.g., developing a solid base in the language to be written) and attend less to those areas (e.g., concepts of print) that have proven to be less troublesome. The following recommendations follow from this position.

1. Continued emphasis on creating rich literacy-learning environments

As the central purpose of using language in any form is making meaning, it is now a generally accepted principle that early literacy experiences should be relevant, purposeful, and functional for the learner. Programs should be designed to provide daily opportunities to experiment with reading and writing, linking literacy with experiences and the active use of language ( Teale & Sulzby, 1989). A well-designed program should incorporate a balance between a whole language focus and skills instruction, taking advantage of the strengths of both ( Pressley, 2006; Stanovich, 2000). And although there are indications that, especially for at-risk learners, the balance of elements in an integrated literacy program may need to be adjusted to provide extra support in areas of weakness ( Xue & Meisels, 2004), there would be nothing to gain by engaging in a deaf education version of the "reading wars."

One of the most positive moves in recent decades has been the move to literacy-rich programs for young deaf children. That said, the challenge will be to design environments that continue to emphasize this richness while at the same time promoting children's attainment of the orthographic and phonologic understandings that are necessary to achieve fluency.

2. Less emphasis on literacy-learning strategies that are easily acquired or less efficacious

As was argued earlier, deaf children are generally on par with hearing children in the very earliest stages of literacy development. They quickly learn that text carries meaning and that it differs from drawing. They readily acquire concepts of print such as directionality, letter matching, and word spacing. This set of "outside in" skills is important, but the evidence suggests that we need not focus inordinately on them in our teaching or research as deaf children generally do not struggle to develop them.

There is also a danger in putting too much emphasis on the memorization of sight words as an approach to early literacy learning-a common feature of many early literacy programs for deaf children. A reliance on this strategy deprives the child of the chance to use those analytic and hypothesis testing skills that involve syntax and meaning, and it promotes a false sense of what reading and writing really is (Donaldson & Reid, 1982). It is not a strategy that, by itself, leads to fluent reading and writing, even though it is relatively easy to teach and allows for some early success.

It is also problematic to talk about deaf children as "readers" when they only focus on key words to gain meaning from a text. Rottenberg (2001) describes Jeffrey, a preschool-aged child she observed for an academic year, as an independent, competent reader when the evidence indicates that he would sign one word "sick" when reading the text "I'm sad when I'm sick" (p. 274). While fluent reading rests on more than decoding every word, children cannot paraphrase something that has not been interpreted correctly to begin with, and it is certainly the case that as texts become more syntactically and semantically complex, there is an even greater need to read each word with care ( Donaldson & Reid, 1982). Pressley (2006) cautions against promoting an approach that gives a priority only to meaning cues in word recognition. He suggests that this is a very dated idea that persists despite evidence that sounding out and decoding at the word level promotes word recognition and comprehension better than an emphasis on semantic contexts.

3. More emphasis on aspects of literacy learning that are problematic

There are two areas, fundamental to success in learning to read and write, that need to be given more explicit attention as we design and research early literacy programs for deaf children. These are (a) developing a foundation in the spoken and/or signed mode of the language to be written (i.e., English) and (b) focusing on those strategies that allow children to make the most effective connections between face-to-face language and text. These are categorized as problematic for two reasons. They represent the set of abilities and skills that are most challenging for deaf children to master and there tends to be disagreement in the field as to the extent to which they are necessary (or possible) for early literacy learning in this population. That said, we cannot ignore, in our pedagogy and research, that which has shown to be pivotal in the development of early literacy learning with hearing children. And although gains made may not always bring them to the same level as their typically developing peers, it has been shown that hearing children with disabilities benefit from explicit teaching in these areas (O'Connor, Notari-Syverson, & Vadasky, 1996).

The Need for an English Language Base

The case has been made that the challenge facing young readers and writers is to come to see text in terms of their face-to-face language. This requires that they have enough lexical and syntactic knowledge of this language to effectively bring it to bear on the text. Although debate continues as to what constitutes a requisite threshold level of spoken language, it is widely accepted that children with a stronger spoken language base are better placed to develop early literacy abilities than those with weaker abilities in this area. Beyond necessary knowledge of the core grammar of English ( Gee, 2001), this oral preparation for literacy includes discourse experiences that feature decontextualized language use. For example, during sharing time or show and tell, children would be encouraged to present information in ways similar to a well-written paragraph, assuming no prior knowledge on the part of the listener, being explicit by including a topic sentence and only relevant details, and employing the requisite complexity of syntactic structures ( Snow, 2001).

The implication for deaf children is that early literacy programs must place a premium on the development of face-to-face English. For oral deaf children or those using some form of English-based or contact sign, this also means putting more emphasis on using their face-to-face language in a wide range of contexts and in ways that will provide access to the English vocabulary and structures commonly found in text. For deaf children who use ASL as their first language, the challenge is that the text does not represent the language they are signing. There is no logical way for them to see their face-to-face language in terms of English print, as English print was never designed to represent ASL in the first place. Conversely, the text, while providing a model for thinking about signed language (e.g., print brings the nature of finger spelling into conscious awareness), does not yield the same set of understandings as it does for users of English (e.g., a sign is not always equivalent to a word). Therefore, unlike the situation for the child speaking or signing in English, there is a breakdown in the systematic relationship between talk and text.

To be clear, this is not an argument for or against ASL as a first language for deaf children, but rather a questioning of the aptness of the metaphor that there are bridges that link a natural signed language such as ASL and print literacy in English. Signing in one language and writing in another is a translation activity that goes far beyond what we are asking of young hearing writers in early literacy programs. Therefore, it should not be surprising that this is a problem far too complex for most young deaf writers to solve. In fact, many of the bridges that have been proposed to make the connection between the two languages are actually English based. They do not represent a bridge between languages, but within a language, that is, from one representation of English to another (e.g., using cued speech, visual phonics, finger spelling, or signed English to mediate the construction of text). In other words, all these bridges depend on some knowledge of English to begin with, and therefore in ASL-English bilingual programs, attention must be paid to the ways in which face-to-face English is developed. It is not the presence of ASL but the absence of some form of face-to-face English that is at issue, and the challenge for educators in bilingual programs is to sort out the balance between the two languages that allows for sufficient opportunities for the development of both (e.g., see Swanwick [2002] for a discussion of the use of British Sign Language and manually coded English in a bilingual setting).

To make a productive connection between two representations (spoken/signed and written), the representations must be in the same language. If they are not, we are asking deaf children to make sense of an impossible problem. Although it has been demonstrated that deaf children do the best they can to make relationships where possible (e.g., handshapes mapping onto English letters), it has not been demonstrated that this gets them to standard English beyond the level of conventional spelling. If we accept the premise that an English base is necessary, this raises questions as to how to provide meaningful access to face-to-face English in a manual mode for the purposes of learning to read and write (see also Marschark & Harris, 1996).

Strategies

Beyond a spoken or signed base in the language to be written, deaf children need to be in command of those strategies that are most effective in making the link between language and text. This is an issue that researchers and educators in the field have been attempting to address for some time, and although numerous options have been proposed (see below), there is little consensus as to the strategies that may be most efficacious for deaf learners. For hearing children, the "inside out" strategies related to phonological processing have been shown to be central in linking talk and text, and in a review of the issue with respect to deaf learners, Schirmer and McGough (2005) note that it is not yet known whether explicit training in phonological awareness is effective. They speculate as to whether finger spelling and sign language can allow deaf readers to make visual connections to text in ways that are comparable to what phonological processing affords hearing children via the auditory channel. In her most recent comment on the subject of finger spelling, Padden (2006) argues that it should be a part of the preschool child's language development, suggesting that it sets the child up for later using finger spelling as a link to English words and the development of literacy. She describes this as pushing toward "a convergence of skills where the skill of finger spelling is aligned to the skill of reading and written spelling" (p. 197).

Although there are benefits in making these links, the benefits are limited if the goal is to make links between language and text that are commensurate with those that hearing children make via phonological processing. Finger spelling is very efficacious at the word level where the relationship between manual representation and orthography is logical and systematic, and this may help account for the success that deaf students have in achieving standard spelling. It could also be useful for highlighting discrete aspects of spoken language, bringing these aspects into conscious awareness and helping to make the link between talk and text more explicit (e.g., using the manual alphabet to mark word endings such as "s" that can be difficult to hear, almost as a form of manual highlighting). However, it is unlikely that finger spelling alone would be an adequate substitute for the range of "inside out" phonological processing strategies that hearing children employ.

This emphasis on the primacy of phonological processing should not be taken as an argument for intensive speech programs for all deaf children or for a move away from manual communication. Rather, it raises questions as to the nature of the phonological processing strategies that young deaf children, who must rely on sources other than audition, could be taught in order to afford them the benefits that hearing children gain via this route. It may well be that some of these strategies are unique to deaf learners and are a consequence of the idiosyncratic ways in which signed and spoken modes can be combined.

Trezek and Wang (2006) make a convincing case for the use of visual phonics within the context of a phonics-based reading curriculum, and Harris and Moreno (2006) and Campbell ( Marschark, Siple, Lillo-Martin, Campbell, & Everhart, 1997) suggest that a phonological code can be derived through speechreading. Arguments have also been made for the efficacy of cued speech ( LaSasso, Crain, & Leybaert, 2003) or the integration of numerous input sources (articulation, speechreading, finger spelling, and writing) as routes to the acquisition of phonological information ( Leybaert, 1993; Mayer, 1998). Investigating the viability of any or all of these routes (or others that are yet to be defined) should become a focus of our teaching and research.

Conclusion

I share Williams' (2004) frustration with the relative paucity of direct evidence available with respect to the early literacy development and experiences of deaf children. It stands in stark contrast to the incredible volume of research on the early literacy development of hearing children-a body of research that includes studies of children with disabilities and those who are second language learners. Admittedly there are fewer studies that focus on the latter two populations, but all available evidence suggests that there is little difference among these groups as to the sets of skills and abilities needed to develop conventional literacy. Differences tend to rest on the nature of educational programming and interventions specific to each group-differences that are driven by the particular strengths (i.e., an established first language literacy base in L2 learners) or weaknesses (i.e., learning disabilities related to auditory processing) that children exhibit. But there is no indication that these children bypass or do not need to acquire the same understandings (e.g., a knowledge of the language to be written, phonological processing skills) as all other young literacy learners. In this paper, an argument has been made that young deaf literacy learners are no different in this regard and that what really matters for them in the early stages of literacy learning is not very different from what matters for their hearing peers.

It would be instructive then to bring the body of research evidence from hearing children to bear in articulating a theoretical model of deaf children's early literacy development. To this end, in this paper, Olson's (1994) model has been used as a framework for conceptualizing the challenge young deaf children face in learning to read and write. Olson's emphasis on the interdependent relationship between language and text allows for thinking about the ways in which deaf children can (must?) make the link between speech/sign and print, suggesting possibilities for future research.

And while there is absolutely a need for more research on the early literacy development of deaf children, this research must be focused more judiciously. Some of these focus areas have been identified in the final sections of this paper, with a particular emphasis being put on the third stage of early literacy development where children make the transition from emergent to conventional literacy. It is vital that we develop a richer understanding of this stage as, based on the evidence we have to date, it is at this point that the development of deaf children is no longer in line with that of their hearing peers. Although it is encouraging to see the field moving in this direction (e.g., recent studies on the efficacy of systematic phonics instruction), much remains to be done. Although the challenges can be daunting, the research possibilities are exciting and have the potential to meaningfully inform practice.

As a final word, there is also a need to engage in studies of early literacy development that hold researchers accountable for reporting whether deaf children ultimately learn to read and write at an age-appropriate level. At the risk of sounding strident, it is frustrating to read research on early interventions that do not track children over time to find out if they actually learn to read and write at grade level or to encounter claims that a strategy has been successful even when the evidence indicates that less than age-appropriate literacy has been achieved. Attending to literacy outcomes must feature in both our teaching and research because in the end what really matters in the early literacy education of deaf children is that they do learn to read and to write.

No conflicts of interest were reported.

References

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What Really Matters in the Early Literacy Development of Deaf Children | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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Earlier identification of hearing loss allows for earlier intervention and raises expectations that increasing numbers of deaf children will develop language and literacy abilities that are comparable to their hearing age peers. 1 By implication, such expectations focus attention on what happens in the early years of literacy learning as these experiences have been shown to be critical to future success for hearing children.

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Cochlear Implanted Pupils in Scottish Schools: 4-Year School Attainment Data

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Abstract

The purpose of this article is to report 4-year aggregate data on educational placement and placement migration, language and communication characteristics, and school attainments among deaf pupils with cochlear implants in Scottish education between 2000 and 2004, as a subpopulation of a national study that is recording the school achievements of deaf pupils in Scotland. This introduction will detail the study from which the data are drawn and introduce those three themes within the context of school education for deaf pupils with cochlear implants in Scotland. The Method section will describe how data are collected within that study and how the relevant data are reported for the purposes of this article.

The Achievements of Deaf Pupils in Scotland (ADPS) project has established a database for monitoring the educational outcomes of deaf children in Scottish education over time. Most akin to Gallaudet University's longstanding demographic "Annual Survey of Deaf and Hard of Hearing Children and Youth" ( Allen, 1986, 1992; Jensema & Trybus, 1978; Karchmer & Mitchell, 1993; Schildroth & Hotto, 1993, 1996; Trybus & Karchmer, 1977), the project is not a panel study focusing on a constructed sample of deaf children in any particular birth cohort but a national cumulative data set aimed at monitoring the progress of the entire population of deaf pupils as they stream through Scottish education, starting from the year 2000.

One notable feature of the ADPS data is therefore that a number of deaf pupils migrate in or out of the database in June (the census date) of each year. Much like the biennial United Kingdom-wide surveys by the British Association of Teachers of the Deaf (BATOD), the ADPS data are reported by education professionals and targeted through the local authority support services and schools for deaf pupils across Scotland. The cooperative basis of reporting by teachers-as distinct from compulsory reporting by schools for the yearly statistical bulletins published by the Scottish Executive-is the main cause for missing cases in any particular year of ADPS returns, and therefore the ADPS office makes repeated efforts to clear up nonreturns in direct dialog with respondents.

A second important feature of the ADPS data set is that the project collects information on all educational attainments for all known deaf pupils, through a follow-up survey in September of each year. This article focuses on the national assessments and examinations of cochlear implanted deaf pupils, as a particularly meaningful "real-life" measure of pediatric cochlear implantation outcome. After all, pediatric cochlear implantation, as an intervention in the sociolinguistic and cognitive development and maturation of profoundly deaf pupils, quite rightly attracts real discussion between deaf people, parents, and professionals. This also motivates research on outcomes, albeit typically within the areas of health and education separately. Although relevant literature is discussed in this article, an international literature review has recently been published (Thoutenhoofd et al., 2005), and that publication provides a detailed analytical background to work in this area.

The majority of studies have focused on spoken language development outcomes, often measured through tests conducted in controlled settings. There is scant evidence on actual school attainments of pupils with cochlear implants (Thoutenhoofd et al., 2005, p. 246), and this article will seek to correct that to some extent, for Scottish education. There are two points of contact between this report and other published results and those concern measures of placement and communication ("mode"). Both these measures are data-artifacts: their meanings vary among reports. But because ADPS collects the BATOD survey data for Scotland, within studies based on ADPS and/or BATOD data the measures contain a considerable degree of designed equivalence.

A third important feature of the ADPS project is the ascertainment of deaf pupils: which pupils are "deaf"-what is deafness? Many studies have a cutoff point for the lowest level of hearing loss in the construction of the sampling frame; the measure is intended to reflect the point at which there is assumed to be no real implication of deafness on the ability to hear spoken language. Although this provides a study with a straightforward boundary of the sampling frame, it does not take account of deafness as a social phenomenon.

What matters in education is not how well a pupil can hear but whether a pupil has unimpeded access to the social, interactive process of teaching and learning. In line with the current legislative framework, the concern is with the nature of the provision, respecting diversity among pupils. In that context, the level of support available in the educational management of deaf pupils is a more pragmatic and more holistic determinant of sample frame boundary than is hearing loss level (Karchmer & Mitchell, 2003, p. 22). So, whereas a 1998 United Kingdom-wide study (Fortnum et al., 2001) included only those pupils with permanent bilateral hearing loss of >40 dB-which would equate with 1,070 deaf pupils, or 61% of pupils in the 4-year ADPS data set-the ADPS project instead includes those deaf pupils seen twice or more per year by their educational support service regardless of the nature of their hearing loss, and for Scotland that number totals 1,752 pupils. Having said this, the relative number of pupils with comparatively less need for support that are reported does vary across local authorities because of local differences in levels of resourcing.

Placement

Given the intrinsic focus within pediatric cochlear implantation on achieving functional hearing ( Thoutenhoofd et al., 2005, p. 19), there is self-evident alignment of its objectives with those of national education policy-most notably the current "presumption of mainstreaming" pupils ( Scottish Executive, 2002)-because this currently implies a normative education that is monolingual and spoken in delivery. A number of studies have focused on mainstream placement as an outcome indicator (Archbold, Nikolopoulos, Lutman, & O'Donoghue, 2002; Archbold, Nikolopoulos, O'Donoghue, & Lutman, 1998; Bennett & Lynas, 2001; Daya, Ashley, Gysin, & Papsin, 2000; Fortnum, Marshall, Bamford, & Summerfield, 2002; Nevins & Chute, 1995), and some studies also take into account the cost of educational support offered in compensation for disadvantage present within the system ( Francis, Koch, Wyatt, & Niparko, 1999; O'Neill et al., 2000, 2001; Wyatt & Niparko, 1996). In those studies mainstream placement is associated with relative independence from support, leading to claims of cost-savings over time in favor of cochlear implantation.

Within this "health economic inclusive logic" the aim is for deaf children to receive their education in mainstream settings without much need of support, in the implied sense that it is cheaper to adapt the pupil to the education system-including the cost of surgery-than to adapt the education system to the pupil. Obviously, given the demonstrable migration of deaf pupils from segregated into mainstream settings over the last decade, along with the associated closure of some schools for deaf pupils, studies that report such migrations among pupils with cochlear implants partly confirm a similarly economizing trend supported by national education policy and therefore not necessarily an outcome of cochlear implantation.

During the period in which multichannel cochlear implants became widespread, a United Kingdom study that focused on mainstream setting reported that most of the 16 pupils with cochlear implants included in the study were performing in the top half of their class, albeit with the support of speech/language therapy in most cases ( Nevins & Chute, 1995). This study was based on observation and teacher interview data rather than on actual school attainments. Subsequently, results from the Nottingham Implant Centre Programme have noted a significant shift from deaf school placements toward mainstream placements for deaf pupils with cochlear implants, with age at implantation and duration of deafness as significant predictors of later placement ( Archbold et al., 1998).

More recently, two studies ( Archbold et al., 2002; Fortnum et al., 2002) confirmed the effect of cochlear implantation on education setting in favor of mainstream placements, noting that the shift in placement roughly equates the pupils with cochlear implants with severely deaf pupils of the same age. A similar comparison has been reported in an Australian study ( Blamey et al., 2001). It has also been reported that the rate of improvement in speech perception ability is higher among pupils with cochlear implants who moved (or remained) in a mainstream setting than among pupils with cochlear implants who did not, although that study did not observe a significant move toward mainstream settings among the sample ( Daya et al., 2000).

Communication

Early results of studies focusing on communication suggested that multichannel implants produced gains in auditory speech perception that made pupils with cochlear implants similar, in their performance on spoken language tests, to severely deaf children (Boothroyd & Eran, 1994), and those conclusions were still reached much more recently, reporting average performance gains equivalent to about 28 dB in hearing threshold ( Blamey et al., 2001). The measure of communication is less focused on shift from one form or mode of communication to another, as on comparisons between deaf populations with different communication preferences, or settings characterized by contrasting communication policies. The latter is a commonplace among U.S. studies, and some among them follow on from an earlier series of studies that favored oral programs, in part due to their focus on spoken language outcome measures (e.g., Geers et al., 2000; Moog & Geers, 1985, vs. Moog & Geers, 1999).

Other studies have somewhat predictably confirmed the advantages of oral communication mode with respect to spoken language outcome measures (e.g., Cullington, Hodges, Butts, Dolan-Ash, & Balkany, 2000; Hodges, Dolan Ash, Balkany, Schloffman, & Butts, 1999). In the United Kingdom, the Nottingham Cochlear Implant Centre Programme also reported that pupils with cochlear implants in oral programs outperformed pupils with cochlear implants using signed communication in total communication programs with respect to a series of spoken language measures. The authors report migration among total communication and oral programs in both directions, concluding that early, preimplant signing is "not a disadvantage" to developing later oral skills ( Archbold et al., 2000, p. 263). Separately, three studies have noted that the majority of pupils with cochlear implants using a form of sign language prior to implantation continue to do so postimplantation ( Coerts & Mills, 1995; Preisler, Ahlstrom, & Tvingstedt, 1997; Tye-Murray, Spencer, & Woodworth, 1995).

A constant challenge in the construction of a valid measure of communication mode is that its subcategories will rarely describe a clearly defined, coherent set of practices with respect to either quality or quantity of both spoken and sign(ed) language interactions. A further consideration is that pupils in specific programs are not guaranteed to be consistently exposed to whatever the program supposedly entails, despite the best efforts of the service as a whole. And finally, the widespread use of "Total Communication" as one of the subcategories covers a particularly variable set of practices: its meanings are neither fixed nor shared. Moreover, it is occasionally used to describe a practice within which sometimes a range, and sometimes just one communication option is used, depending on the perceived ability of individual pupils-mostly in the linguistically counterintuitive sense that the most able pupil is the most monolingual (English) pupil.

The ADPS data set therefore distinguishes between four different kinds of Total Communication programs. Even so, some respondents are perhaps quite rightly reluctant to have their provision categorized within a spectrum that by nature of its range allows for considerable misinterpretation of findings. In one or two cases this resistance therefore logically persists despite an apparent fit between what respondents say their service provides and the response categories open to them. Broadly similar provisos must be made with respect to the range of "English monolingual" programs that are equally captured by four separate categories in the ADPS data and also with respect to the sign-bilingual provisions that exist. In summary, from an educational perspective communication mode is a complex construct not always suited to convenient ordering into either mutually exclusive or an exhaustive range of items. That observation needs wider recognition particularly in the sense that it may well be necessary to supplement quantitative measures with more qualitative measures such as direct observation and interviewing.

Method

The ADPS project aims at total ascertainment of deaf pupils in Scottish education, as an ongoing effort since 2000. Relative to the combined reported caseload of support services across Scotland, the demographic surveys attained an average 95.7% of returns across the first 4 years (2000-2004). New entries are notified in the year that deaf pupils start receiving two or more visits by a support service, and the records for each pupil are updated annually until those visits stop (e.g., because the pupil exits education or moves out of Scotland). The reporting of pupils' names is voluntary; pupil tracking is achieved through a unique identifier (ID) assigned to each new entrant. As well as a unique ID each pupil is given a secondary identifier that pinpoints the support service and each pupil within that service; this secondary identifier therefore changes as a pupil migrates between different services, but the database constructs a "histogram" of all previous IDs. Currently, the surveys also collect the Scottish Candidate Number for deaf pupils undertaking national qualification assessments, so that their attainment data can be downloaded from the Scottish Qualifications Agency (SQA) each year-as collated through the national network of administration systems used within schools.

As well as a range of demographic data (date of birth, gender, ethnicity, home postcode, school address) the surveys collect information on audiology (audiograms, etiology, type of deafness, aids to hearing and the extent of their use, etc.), additional needs (a range of learning needs and medical conditions), language and communication data (language used at home and in the classroom, service policy), placement data (including full-time and part-time placements and funding arrangements), as well as outcome data (baseline and reading-age assessments, 5-14 Curriculum tests and subjects, Scottish Credit and Qualification Framework outcomes, all national examinations, and Deaf Studies, British Sign Language [BSL], and lipreading qualifications). In addition, the surveys collect data on levels and range of support provided by the support service to each pupil, special assessment arrangements in place for each individual attainment, and the language(s) used in assessment.

Data are entered in the ADPS office into a secure dedicated database in which all names (of pupils and respondents) are hidden from reporting functions. At this time, hard copy returns are stored for 3 years postcompletion; they are used for checking data integrity and consistency. On a few occasions, entered data have needed corrections post hoc, so that reported figures for any year may vary slightly between subsequent publications. A full project description and detailed data reporting tables can be obtained from the ADPS Web site.

Study Population Details

There are 105 primary school deaf pupils with cochlear implants in the 2000-2004 data, 65 male and 40 female. Among them 87 (82.9%) have a Record of Needs. Their average age is 8.06 ( Mdn = 9.0, SD = 2.1), ranging from a pupil aged 5.04 in P1-"Primary 1"-to a pupil aged 12.06 in P7, the last year of primary in Scotland. On average they were 3.02 years of age when implanted ( Mdn = 3.03, SD = 1.6), and they have an average of 4.01 years experience with their cochlear implant ( Mdn = 4.08, SD = 1.9). On average across 4 years, there are 3.7-year records on file for these pupils.

Forty-seven secondary school cochlear implanted deaf pupils are present in the data, 19 males and 28 females. Among them 44 have a Record of Needs. Their average age is 14.07 ( Mdn = 14.06, SD = 1.9), ranging in age from 12.05 for a pupil in S1-"Secondary 1"-to 18.02 for a pupil in S6. On average they were aged 7.07 when implanted ( Mdn = 6.06, SD = 4.1), and they have 5.03 years experience on average with their implant ( Mdn = 5.05, SD = 3.0). On average across 4 years, there are 2.9-year records on file for these pupils.

Within the group of 152 pupils, 5 have an Asian ethnic background, 5 are reported as Black ethnic origin, 2 from a mixed background, and 4 more are reported as "other"; 139 deaf pupils with cochlear implants in the group are from a White ethnic background. Due to the broad reach of the ADPS project there is no scope to collect detailed data on socioeconomic status (although such information has been sought from a self-selected subsample of parents in one year), nor are data collected on measures that are common in health-based cochlear implantation studies, such as type of device and detailed medical assessments.

Data Reporting

The study population as detailed above is based on a search of the most recent record on file, for each pupil, across the years 2000-2004. This ensures that pupils who were not reported for each of the 4 years (but perhaps for just 1 or 2 or 3 years) are nevertheless included in the study-hence the earlier comment that this is not a cohort or panel study: Pupils for whom there is information for less than the full 4 years are not deleted from the reporting here as "missing cases" because this would have introduced a sampling effect. The average number of records on file cited above is therefore an indication of the number of questionnaire returns within that period for the pupils known to the database. A pupil cannot have more than one record per year, and therefore there can be no double counting of individual pupils.

For the descriptive purpose of this study, the results of 4 years have been combined. This is done for the reporting of changes in placement, where all instances of placement change between 2000 and 2004 are reported; for the reading-age measure, where the data for all 4 years combined are distributed by chronological age; and for the school attainment measures (National Tests and Standard Grades), where the data are distributed by level of attainment. In this form of reporting, data for more than 1 year will be included for many of the deaf pupils, and therefore it is important to keep in mind that no actual longitudinal findings can be derived from those results. The 4-year aggregation of measures concerning language and communication is detailed within that particular section.

Because the data for 4 years have been aggregated and because some of the measures reported here are constructs in which more than one variable are combined (such as changes in placement and communication mode), there has been no attempt here at parametric statistical processing of data. However, statistical analysis of key measures within the ADPS data set as a whole is in progress, and findings from that program of work will be reported in due course.

Categorization of Placement

Each pupil of school age is classified by respondents into one full-time placement or more than one part-time placement across eight categories. The majority of deaf pupils can be allocated to one of four generalized categories for the purpose of broad comparison with other studies. Among the deaf pupils of school age with cochlear implants ( n = 156), 144 were in full-time placements: 57 were placed in a mainstream school, 14 in designated integrated provision, 48 in a school with an attached unit or resource base, 22 in a school for deaf pupils, 3 in special needs provision, and 1 pupil was educated at home in the most recent year on record for each pupil in the 2000-2003 period. The remaining 11 pupils were in combined placements. For the purposes of comparison, only pupils placed in special needs settings and outside the school system are excluded from the reporting. Therefore the maximum number of deaf pupils with cochlear implants reported on here is 152, counted by their most recent record in any year between 2000 and 2004, that is, subject to migrations. In Year 1 (2000-2001) this covers 101 pupils (98 in full-time placements), 122 in Year 2 (114 in full-time placements), 127 in Year 3 (118 in full-time placements), and 127 in Year 4 (122 in full-time placements); the exact population does vary from year to year because this project is not designed as a cohort study.

Table 1 shows this population distributed by year group in each of the 4 years included in this account. As is clear from the data in this table, the numbers vary across time (i.e., these are not cohort data). This variability across years is explained by migration of pupils in and out of the database, pupils being lost to follow-up (in particular in 2003), and in fewer cases by the over-time changes in pupil status, as operations change profoundly deaf pupils known to the database into deaf pupils with cochlear implants-the 4-year "cohort" represented by the gray cells shows this happening between S3 in 2002 and S4 in 2003.

Categorization of Communication

Within Scottish education, a range of communication strategies can be used with deaf pupils, in theory, regardless of actual placement. Most deaf pupils will be exposed to spoken and written English. In addition, the use of so-called sign systems (methods of communication that combine English with a manual lexical or sublexical component that is either invented or borrowed from BSL) is also widespread, in particular Sign Supported English (SSE), but a system called Signed English (SE) is also reported. Another sign system called Paget Gorman was previously commonplace in one school for deaf pupils. BSL, a sign language used among members of the deaf community in Britain, is a living language-that is, its use does not derive from the education system but from generations of deaf people themselves-and may also be used, although there is no actual requirement for teachers of deaf pupils to be fluent in anything but English.

English, sign systems, and BSL can be used in various kinds of combination in approaches that are collectively called Total Communication. Support services and schools for deaf pupils may have a language policy spelling out which form(s) of communication staff should use with deaf pupils. Language policies may or may not be stringently enforced, but there is no actual obligation to have such a policy in place. Within ADPS, English monolingual refers to a language policy in which sign systems and BSL are absent. Natural aural policies refer to an approach in which pupils learn by listening in a suitable auditory environment, without lipreading or specific manual input but with extra help in the creation of listening and talking opportunities.

A number of questions are aimed at categorizing the communicative characteristics of each pupil and their environment, including languages/language mediums used with the pupil while in school (including the nature of any Total Communication approach used), the main language deemed to be used in the pupil's home (this is an education staff judgment), the language/language mediums available through the service's support, and any language policy that might operate. The data relating to the latter two measures were collected in early 2005 and linked to the pupil records. All other communication measures are collected through the annual surveys, but data from Year 1 (2000-2001 school year) are excluded because the measures collected for that year are not exactly equivalent to those collected in subsequent years.

The 5 pupils with an Asian ethnic background also identify the pupils with whom Urdu or Punjabi is spoken at home. English is reportedly spoken at home in 94% of cases-in 84.4% of those without any form of signing. BSL is reportedly used with 19 deaf pupils with cochlear implants at home (see Table 2).

In the classroom, spoken English is equally used with 94% of the pupils, and in 46% it is reportedly the only form of communication used with the deaf pupil. This would mean that the use of signed modes of communication is considerably more widespread in the classroom than at home: this is true for BSL (40%), SSE (38.7%), and SE (7.3%), which is reportedly never used at home. What this also suggests is that, for a number of deaf pupils with cochlear implants, communication varies between home and school, and this will be an important observation.

Responses to the detailed questions in the ADPS surveys flag up the complexity of capturing the communicative environment of pupil/school/service in a single robust measure. The survey asks on an annual basis what forms of communication, including different definitions of Total Communication, are used with the pupil in school. Separately, in early 2005 support services were invited to describe the language policy (if any) advocated by the service, and their responses are distributed to the group of deaf pupils with cochlear implants in Table 3.

Deaf pupils with cochlear implants are reported by 27 of the 44 support services across Scotland. The support service of 30 pupils report not having any specific policy, and in the case of 3 pupils their support service provided no detail. A service advocating a Total Communication approach accounts for the largest number of pupils ( n = 67), whereas the support service of nearly half of deaf pupils with cochlear implants ( n = 73) ought to allow, in principle, for the use of at least "some" BSL or support a BSL/English bilingual approach. However, it must be kept in mind that the language policy of a service (where one exists) does not necessarily equate with the actual modes and forms of communication used with individual pupils within the service, and even less that such are used consistently over time, by different classroom and support staff.

Indeed, in 12 cases the kind of Total Communication that was reportedly used with the pupil in the classroom (i.e., data in Table 2) differed from the language policy that was reported by the supporting service (i.e., data in Table 3) in important, sometimes contradictory respects. Moreover, the range of placements and schools in which deaf pupils can now find themselves also mean that the communicative environment (such as the range of communication used by deaf pupils' peers in different classrooms and settings, and by different teachers with varying levels of aptitudes and qualifications in forms of signed communication), as well as their own language preferences, can vary a great deal between individuals who are in principle subject to the same language policy or are in the same type placement.

Engagement with respondents over time within the ADPS project suggests that the contemporary complexity of educational programs defeats attempts at broad categorization of communication with pupils into a single robust measure; it involves dealing with layered subtleties of distinction that respondents consider important and with both synchronically and diachronically variable practice, while also needing to account for the antecedent effects of pupil migrations between services, types of placements, and individual schools. This observation suggests that measures of communication mode that are restricted to the actual time point(s) of study-very often with no more than three or four subcategories, as are commonplace in the literature-contain levels of reduction that ought to be taken into consideration.

Categorization of Reading Age

Each year respondents are asked for the most recent estimate of a pupil's reading age. The measure also includes information on the test used to determine reading age, the outcome score, and the language(s) used in the assessment's delivery. The database calculates the difference between the chronological age and the reading age for each pupil in months. Because the ADPS project does not control the assessments (it reports on assessments recorded for educational purposes), it must be noted that a wide range of different tests are being used across Scotland, including not only tests designed to measure reading age but also more informal measures.

In contrast with controlled-condition research designs in which pupils' assessment scores are further cross-analyzed, the variety of tests used has fewer consequences for the practice-driven ADPS concerns because what this reading-age measure records is a diagnostic by which teachers may intervene in the progress that the pupil is making; and at that level of construct, all types of reading-age assessments produce equivalent outcomes. Nevertheless, it is true that the factual basis for that teacher judgment varies in type and quality across the reported responses for this measure.

Categorization of School Attainments

In Scotland, the National Tests of the 5-14 Curriculum (so-called because it is appropriate for pupils aged between 5 and 14) provide the opportunity for comparing national attainments with attainments of subpopulations of deaf pupils. The National Tests (covering reading, writing, and mathematics) are part of a structure of continuous assessment, where specific attainment levels are associated with age-appropriate performance. In other words, pupils supposedly work toward, and attain, higher performance in a subject at their own pace. Therefore, National Test results are not based on an exam structure but on a local assessment by pupils' own teachers of their level of performance as indicated by a bank of materials at six levels (A-F) of study that are laid down nationally. The assessments confirm the teacher's judgment that the pupil has covered the elements and targets of the curriculum subject at a given level, that the work is consistent, and that the pupil can complete the assessment with the appropriate degree of independence.

The six levels of performance range from a baseline, Level A, up to Level F, the indicator for the highest level of performance (note that this is the opposite of grading systems used in North America). Levels A-D are mostly associated with Primary, whereas the top levels E-F are mostly associated with Secondary. With those six levels, the 5-14 Curriculum covers all of Primary (Year Groups P1-P7) and the first 2 years of Secondary (Year Groups S1 and S2).

Standard Grades are generally taken over 2 years of study in the third and fourth year of Secondary, when pupils are aged 14-16 (Year Groups S3 and S4). However, there are many cases in the ADPS data set of deaf pupils taking Standard Grade exams in S5 and S6 and all those are included in the reporting. Pupils take up to eight subjects including Math and English. There are three levels of study: Foundation, General, and Credit, and pupils usually take exams at two levels: Foundation/General or Credit/General. Local assessments, including projects and portfolio-based work, can also count toward the award. For Standard Grades, the Scottish Executive reports national figures as a distribution of the total number of Foundation, General, and Credit awards made.

Placement and Communication Findings

Changes in Placement

Table 4 shows the pattern of migration among the 152 cochlear implanted deaf pupils, regardless of whether they were in primary or secondary, or how many years they are on record. Because 3 pupils moved more than once, there are 155 cases of placement listed in total. The bold numbers in the matched cells represent those pupils who, across the years on record for them, have not moved between different types of placement.

The ADPS data do not show an overall pattern of migration into the mainstream for deaf pupils with cochlear implants in Scotland. Although 3 pupils originally placed in a hearing impairment (HI) unit or resource base attached to a mainstream school and 3 pupils from a school for deaf pupils moved into mainstream placements, there are comparatively greater numbers moving in the inverse direction over time. Over time, 10 deaf pupils with cochlear implants in total moved to schools for deaf pupils and 10 moved to a school with an attached unit/resources base from a mainstream setting or designated integrated placement. Of the 7 pupils who moved from a full-time mainstream school placement to designated integrated provision, 3 moved to an HI unit/resource base another year later. In total, 19 pupils moved from a mainstream setting to other types of provision.

On balance, the 4-year figures suggest a trend for this Scottish population that does not necessarily support the move to participation in inclusive schooling intended by current legislation and inclusion policy: Although 76% of deaf pupils with cochlear implants placed in the mainstream in their first year on record have remained in a mainstream setting, there are more deaf pupils with cochlear implants moving from mainstream to other settings over time than are moving into mainstream settings from other types of provision. There are also more pupils moving from a unit/resource base to a school for deaf pupils than vice versa. Moreover, when broken down into figures for individual years, this pattern reveals itself to be typical for deaf pupils with cochlear implants only; none of the other groups of nonimplanted deaf pupils categorized by their hearing loss level match this trend.

This reporting is not sensitive to those deaf pupils with cochlear implants who may have changed placement either just before or during the cochlear implantation process. It must also be borne in mind that the changes in placement that were counted imply different kinds of change for pupils in different localities and situations.

Placement (Full-Time Only) and Communication Mode

Out of the 150 deaf pupils with cochlear implants for whom there is detail on the languages and mediums they use in their education (2001-2003), 139 are in full-time placements, and for those pupils Table 5 shows a breakdown of the language deaf pupils with cochlear implants reportedly use within their placement.

The deaf pupils with cochlear implants placed in settings referred to as "designated integrated" provision are least likely to use language skills in a sign modality at school. Those in schools for deaf pupils are most likely to use language skills across the spoken/signed modality distinction. The spoken modality of English is not used by all: Our data would suggest that 9 deaf pupils with cochlear implants do not access education through spoken English at all, and one among those is in a full-time mainstream placement-these cases may of course be omissions in reporting by respondents. Another notable finding in the data for Scotland is that among deaf pupils with cochlear implants in schools for deaf pupils, the numbers using BSL and those using English appear to be roughly equal. In both schools with attached unit/resource base and in schools for deaf pupils, BSL is reportedly used by a greater number than are sign support systems based on English (SSE and SE). In the mainstream, sign support systems based on English appear to be far more commonplace than is BSL. Among the 70 pupils in mainstream and integrated placements, 14 deaf pupils with cochlear implants (20%) receive a level of sign-bilingual access, whereas the remainder (56) receive an English monolingual education. In schools with attached unit/resource base and schools for deaf pupils combined, 39 among 69 deaf pupils with cochlear implants (56.5%) receive a level of bilingual access to their education, using BSL as either the first or the second language for classroom interactions.

School Outcome Findings

Reading-Age Assessments

In Table 6, all the reading-age scores for the deaf pupils with cochlear implants reported between 2000 and 2004 have been aggregated into one single table. The number of reading-age scores reported in each year is as follows: Year 1, n = 76 (103 pupils, 73.8%); Year 2, n = 44 (122 pupils, 36.1%); Year 3, n = 34 (127 pupils, 26.8%); and Year 4, n = 24 (127 pupils, 18.9%). The reading age is shown as the number of years that the pupil differs in their reading-age score from their chronological age at the census date. The row of missing cases indicates those reported reading-age assessments from which no reading-age score could be calculated.

The table shows a trend that is commonplace for deaf pupils in general: increasingly wide distribution of reading age for chronological age and a predominant downward curve. These are not cohort data as such but 4-year data combined. However, the distribution does show that older deaf pupils with cochlear implants score further below their chronological age than do younger deaf pupils with cochlear implants. The distribution within this table suggests a negative association of reading age with chronological age, toward a gap of around 3 years by age 11-13 and 4-5 years by age 15-17. That conforms with the trend for the ADPS population as a whole, profoundly deaf nonimplanted pupils included. But as increasing numbers of deaf pupils with cochlear implants who were implanted at a younger age add data points to the chronological age range over time, the prediction is that the resulting pattern of performance will improve because it must be borne in mind that the secondary school deaf pupils in this study were implanted at a significantly older age (7.07 years on average) than the primary school deaf pupils (3.02 years on average).

National Test Results

Table 7 shows 2000-2004 results for the National Tests in English Reading for the national population and for all deaf pupils-excluding only "special educational needs" placements-in Year Group P7. Whereas in P7 pupils should normally achieve Level D, Level A is reported for all year groups as representing the baseline measure of attainment. P7 is the final year of Primary education, although the 5-14 Curriculum continues for two more years in Secondary (Year Groups S1 and S2). In P7 therefore, pupils are normally aged 11.06-12.05.

Although it is always important to keep in mind that some deaf pupils do achieve at levels commensurate with their hearing peers, it is clear from those figures that, on aggregate, deaf pupil attainment falls short of the national average. As an average across the 4 years shown, 72.4% of pupils in the national population do attain Level D or above in P7. For the subset of deaf pupils, that average figure is 37.8%. Hence, the "performance gap" between the two populations is 34.6%-representing the number of deaf pupils who fail to attain Level D in P7, when compared with the national percentage, across the 4-year span.

Table 8 greatly expands the data reported to include all three National Test subjects: English Reading, English Writing, and Math. In this table, 4 years of data (2000-2004) have been collapsed. The deaf population has been restricted to include only bilaterally deaf pupils with hearing losses of ≥20 dB (better ear average) but disaggregated for level of hearing loss with cochlear implanted deaf pupils forming a separate category. The data not only include attainments in P7 but also attainments in all Year Groups from P2 (age 06.06-07.05) to S2 (age 13.06-14.05). Only the first year of Primary (P1) has been excluded because for this year the National Tests are not yet meaningful, and so national data are not reported. The table therefore also shows all reported levels of achievement within the 5-14 Curriculum. The 4-year cumulative percentages shown for the deaf pupils are the difference scores that were explained earlier.

On average, there were 478,931 pupils undertaking National Tests in Scotland in the years 2000-2004 compared to a subpopulation of 715 bilaterally deaf pupils. Among those, 89 (on average) were deaf pupils with a cochlear implant. Within the national population 90.4% attained the threshold Level A, whereas 17.5% attained the highest Level F. The difference scores show that deaf pupils do not attain comparable levels in the National Tests, regardless of year group or level of attainment. Moreover, the difference scores rise as the level of attainment increases: this suggests that, as the level of expected attainment goes up, deaf pupils fall further behind, on aggregate.

It is also worth noting that across all three subjects, most deaf pupils with a mild hearing loss are achieving less well at the lower levels of attainment than are most moderately or severely deaf pupils, or cochlear implanted deaf pupils, but this difference is less pronounced at higher levels of attainment. Table 9 shows this more clearly. Here the separate attainment bands have been collapsed into broader ranges: the primary levels (A-D), the full range (A-F), and the secondary levels (E-F).

Deaf pupils with cochlear implants have lower difference scores than do the profoundly deaf pupils. This suggests that the performance gap with hearing pupils is smaller. This is true for all three subjects, and the difference is very similar for all three subjects when the levels of attainment are banded-around 10% in performance difference-except for the higher levels band of attainment (E-F) in which the number of cases is very much smaller ( n = 38, National Test attainments for deaf pupils with cochlear implants). In Mathematics the attainment of pupils with cochlear implants is most marked, showing difference scores that are comparable to those of deaf pupils with a moderate hearing loss. In both Reading and Writing, their difference scores fall between those of moderately and severely deaf pupils.

In addition, the difference scores of deaf pupils with cochlear implants show less increase as the expected level of attainment rises when compared to those of the profoundly deaf pupils, in particular again in Mathematics. This suggests that they are comparatively less behind at higher levels of attainment. However, it is also important to note that the difference scores for all the categories show that most deaf pupils, including most deaf pupils with cochlear implants, attain below the national cumulative average. And finally, as the expected level of attainment increases across year groups, increasing numbers of deaf pupils, including increasing numbers of those with cochlear implants, fall behind the national average.

Standard Grade Results

Table 10 shows the national data as a cumulative total for the years 2000-2003 and the data for the bilaterally deaf pupils as cumulative totals for the years 2000-2004.

For the national population's attainments, the distribution across Foundation-Credit levels shows attainment in favor of higher level awards, in particular for Credit level awards. For most of the deaf pupils, the number of attainments peak at the General level awards. On aggregate, deaf pupils with severe and profound hearing loss also attained fewer Credit level than Foundation level awards in the 4 years. The deaf pupils with cochlear implants show a distribution that is comparable to that of the national population, with the majority of awards attained at the Credit level of achievement. The number of awards is smaller for this subgroup because there are comparatively fewer deaf pupils with cochlear implants in the S3-S6 Year Groups (43); nevertheless, the central tendency will be robust.

What is also notable, in relation to the earlier comment that those pupils achieved comparatively less well at lower levels of attainment on the National Test results, is that deaf pupils with a mild hearing loss attained more Credit level awards than deaf pupils with severe-to-profound hearing loss. These data are not over-time cohort data but cumulative data, yet it is hard to escape the suggestion that the category of deaf pupils with a mild hearing loss appears to show, to some extent, the "closing" of a performance gap relative to deaf pupils with greater hearing loss, across types and levels of attainment.

Discussion

This descriptive study has looked at 4-year aggregate data (2000-2004) for 152 deaf pupils with cochlear implants in Scottish education aged between 5 and 18. The reporting has focused on placement, communication, and school attainment. Across the 4 years, 76% ( n = 60 out of 79 mainstream placements) of deaf pupils with cochlear implants were placed and remained in mainstream schools. However, beyond those there is a migration both from mainstream to other settings and toward mainstream from other settings, a finding that is relevant in relation to the anticipated effect of national policy's educational inclusion of deaf pupils into mainstream provision.

Among the deaf pupils with cochlear implants in mainstream settings, 20% ( n = 14) receive some level of sign-bilingual access (i.e., communication at school is reported to include some use of BSL). For those deaf pupils with cochlear implants placed in units/resources bases attached to a mainstream school and those placed in schools for deaf pupils that percentage rises to 56.6% ( n = 39). The exclusive use of English with implanted deaf pupils is reported to be far more commonplace at home (84.4%) than at school (46%). Spoken English is reportedly not used with 9 deaf pupils with cochlear implants when at school. Among support services, a Total Communication language policy applies to the greatest number of deaf pupils with cochlear implants ( n = 67), a policy using English only applies to 21 deaf pupils with cochlear implants.

The data on reading age show that older deaf pupils with cochlear implants are further behind in their reading skills, relative to their chronological age, than are younger deaf pupils with cochlear implants, toward a gap of around 3 years by age 11-13 and 4-5 years by age 15-17. However, as the number of deaf pupils implanted at an earlier age expands across the age range that pattern may change.

Deaf pupils with cochlear implants attain levels of performance on National Test assessments that are closer to those of hearing peers than are those of profoundly deaf, nonimplanted deaf pupils. This so-called performance gap is less by 10% across all three National Test subjects, English Reading, English Writing, and Math. In Math in particular, their attainment is comparatively high and more like that of moderately deaf pupils. Moreover, at higher levels of performance the attainment levels of deaf pupils with cochlear implants fall less far behind than do the attainment levels of profoundly deaf pupils without cochlear implants. On Standard Grade performance, the subgroup of deaf pupils with cochlear implants is the only subgroup whose pattern of achievement reflects that of hearing peers, showing a pattern of attainment in favor of higher level awards, and a majority of awards attained at the highest, Credit level of achievement.

The findings on the Scottish population of deaf pupils with cochlear implants reported in this account are based on aggregate 4-year data. Although I believe the trends reported here to be robust-because they also hold for each individual year-there may be an artificial effect caused by collapsing four annual time points: Because the data include the majority of pupils at subsequent time points, any specific characteristics among that population will be inflated within the trends reported. On the other hand, the ADPS program of research aims at complete ascertainment rather than a study sample. Hence, with reference to pupils whose deafness is relevant to full educational participation, the ADPS sampling frame should guarantee the representativeness of the study population while return rates are high.

There has deliberately been no attempt made to statistically assess the interaction between communication mode, placement, and attainment. Although statistical modeling is important work-and yet to produce results in the ADPS program of work-it is difficult within statistical approaches to take full account of the effect of changes over time, including relevant information on prior situation: A change in placement is not only a change in placement but can be a change in friendships, communication, pace of learning, level and type of support available, and often distance from home. To ascribe any changes in outcome to a single causal variable (such as cochlear implantation) would be a matter of faith much like any other interpretation of data. However, it is clear from the findings reported here that deaf pupils with cochlear implants form a deaf pupil subpopulation with some very notable characteristics. First among those is relatively higher school attainment, and it should prove valuable to explore that finding statistically.

The ADPS data do not show a clear pattern of migration toward the mainstream among pupils with cochlear implants. Although the majority of deaf pupils with cochlear implants were in the mainstream at the start of data collection-and have remained there-a number of deaf pupils with cochlear implants have moved from mainstream schools to other types of placement over time. It is worth noting that this finding is relevant to those involved in health-economic cost-utility studies because it means that the true cost of education must, as much for the benefit of deaf pupils with cochlear implants as for that of all other deaf pupils, take into account the fact that there is an apparent need for specialist provision of this type (including of course their role of providing expert advice, practical support, and increasingly also training, to the supposedly "cheaper" mainstream). Such findings make it less likely that cost savings in favor of cochlear implantation can be legitimately based on the privileging of one type of provision over another: it may be convenient to health-economic modeling, but it may equally be based on a flawed understanding of how education works.

A final observation concerns communication mode. Despite the detailed questions asked through the annual ADPS surveys, it remains extremely difficult to categorize the different linguistic environments of deaf pupils in a meaningful way, in particular across data time points. It seems, within the Scottish data, that the majority of deaf pupils with cochlear implants are in educational situations where there is optional or supported use of signed forms of communication; fewer than half of those pupils use English exclusively, and our data suggest that 9 pupils might not use spoken English at all. All this too is worth keeping in mind when the relatively high school outcomes are considered. In combination with placement dynamics and the variable, indeterminate use of "Total Communication" as language policy, this suggests that communication options are an essentially local construct within Scottish education, to varying degrees dependent on local values and beliefs, knowledge, skills, experience, policies, and politics. This also applies to an apparently growing trend for incorporating at least some use of BSL. This aspect most certainly needs further investigation. But where there is indeed progression toward bilingual approaches to teaching and learning, there is probably a concomitant need for BSL as a curriculum subject along with English, and as a first or second language in which deaf pupils-including those with cochlear implants-can choose to develop educationally.

Conclusion

Descriptive findings of 4-year aggregate data from a national study of deaf pupils' school attainments (National Tests and Standard Grades) suggest that deaf pupils with cochlear implants, as a group, achieve at levels of performance that are clearly marked when the deaf population is disaggregated for hearing loss. The deaf pupils with cochlear implants in this study are predominantly based in the mainstream, although more of them moved out to specialist provision over the 4 years than moved into the mainstream. In school, the supplementary use of signed communication and/or BSL is reportedly widespread among those deaf pupils, and there is evidence in the data that the communication in school may often be modally distinct from that used at home, where signed communication appears to be used much less. No attempt at establishing patterns of interaction between findings was made, so it is not possible to deduce from these findings alone that they are attributable exclusively to the auditive benefits derived from the cochlear implant. The findings demonstrate that the ADPS program of work can contribute meaningful data on educational attainment that may be taken into account in future evaluations of real-life cochlear implant benefit for deaf children and deaf young people in Scotland.

I thank my colleagues Marian Grimes, for commenting in detail on the initial draft, and David Brien, for commenting extensively on one of the more tricky tables (on placement migration). I am also grateful for the excellent suggestions made by peer reviewers-and especially for their comments on the organization of the material because reporting data of this kind requires attention to a great amount of detail. Because of that support, any errors, omissions, and confusing statements that remain are my own. I also acknowledge the financial support from the Scottish Executive Education Department for the data collection, and Moray House School of Education (University of Edinburgh) Research Office for financial support in writing this article.

References

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Cochlear Implanted Pupils in Scottish Schools: 4-Year School Attainment Data (2000-2004) | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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The purpose of this article is to report 4-year aggregate data on educational placement and placement migration, language and communication characteristics, and school attainments among deaf pupils with cochlear implants in Scottish education between 2000 and 2004, as a subpopulation of a national study that is recording the school achievements of deaf pupils in Scotland.

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Correlates of Psychosocial Adjustment in Deaf Adolescents With and Without Cochlear Implants

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Abstract

Adolescence is characterized by rapid physiological, psychological, and social development (e.g., Harter, 2003; Kroger, 1996; Noller, Feeney, & Peterson, 2001). Deaf adolescents undergoing these changes also typically confront the challenges of being deaf in a sound-dominated environment not always attuned to their auditory and visual needs, particularly in the school setting. Some authors believe that the academic and psychosocial adjustment of adolescents with cochlear implants (CIs) could be adversely affected because of efforts to "normalize" their status and deny their visual needs (e.g., Ladd, 2003; Lane, Hoffmeister, & Bahan, 1996; Preisler, 2007). Other authors argue that the CI can serve as a vehicle for positive academic achievement and psychosocial adjustment because of greater access to spoken language ( Fagan, Pisoni, Horn, & Dillon, 2007; Geers, 2006; Spencer & Marschark, 2003; Wheeler, Archbold, Gregory, & Skipp, 2007).

The importance of exploring academic and psychosocial adjustment in adolescents with CI is underscored by the dramatic increase in the number of children who have received CI over the past 15 years. According to the three major CI companies, more than 50,000 children worldwide have received a CI to date.1 Currently, approximately 12.6% of students in data reported to the Annual Survey of Deaf and Hard of Hearing Children and Youth for the 2006-2007 academic year are reported to have CI (Gallaudet Research Institute, 2006). This is likely an underestimation, considering these data represent approximately 60% of all schools and programs enrolling deaf and hard-of-hearing students. In addition, the most recent information available from the Annual Survey shows that among both cochlear-implanted and nonimplanted children in the United States, 26.5% are in "special schools or centers" (primarily residential or day schools for deaf children). Therefore, most deaf students presently attend fully or partially mainstreamed educational settings.

Although increasing numbers of deaf children have the potential to hear and use spoken communication after being implanted, they typically require intensive communication therapy in order to maximize their expressive and receptive spoken communication (e.g., Christiansen & Leigh, 2005; Nicholas & Geers, 2006). There is a small but growing number of studies investigating the linguistic and academic achievement of these children which demonstrate that students with CI generally perform better academically than those without CI (for reviews, see Fagan, et al., 2007; Geers, 2006; Marschark, Rhoten, & Fabich, 2007). Whether this superior academic achievement persists over time is not yet established.

A growing body of literature also demonstrates the effects of CIs on auditory perception, speech, and oral language development (e.g., Geers, 2004; Waltzman, 2006). These benefits are moderated by several variables, including age of implantation, duration of implant use, and mode of communication. If implantation results in improved auditory perception and more advanced speech and oral language skills in deaf children and adolescents, this may result in better psychosocial adjustment, especially in the areas of academic achievement and peer relationships.

Psychosocial Adjustment Variables

Social Integration of Deaf Adolescents

A review of research studies of deaf students in mainstream settings indicates that interactions with hearing students can be positive when deaf adolescents are emotionally secure and participate frequently in social activities ( Leigh & Stinson, 1991; Stinson, Chase, & Kluwin, 1990; Stinson & Kluwin, 2003). However, deaf students' peer interactions in the mainstream are not always positive, primarily because of difficulties in communication access or lack of awareness regarding hearing loss among hearing peers ( Antia & Stinson, 1999; Bat-Chava & Deignan, 2001; Eriks-Brophy, et al, 2007; Stinson & Kluwin, 2003). Deaf mainstreamed students often report feelings of loneliness, few close friendships, and lack of contact with deaf peers ( Foster, 1988; Leigh & Stinson, 1991; Stinson & Lang, 1994; Stinson & Whitmire, 1992; Stinson, Whitmire, & Kluwin, 1996). Within public schools, although deaf adolescents typically experience more comfort and emotional security in relationships with deaf peers than with hearing peers ( Stinson, et al., 1990; Stinson & Whitmire, 1992), some do report comfort with hearing peers, depending on social and communication competence ( Bat-Chava & Deignan, 2001; Musselman, Mootilal, & MacKay, 1996; Stinson & Antia, 1999).

Parents of deaf children with CIs generally expect that the CI will facilitate communication and psychosocial adjustment within the family, school, and neighborhood settings. Several studies have used parent perceptions to explore the psychosocial effects of pediatric cochlear implantation ( Bat-Chava & Deignan, 2001; Bat-Chava, Martin, & Kosciw, 2005; Chmiel, Sutton, & Jenkins, 2000; Christiansen & Leigh, 2005; Kluwin & Stewart, 2000; Nicholas & Geers, 2003). In general, these studies indicate that parents see evidence of improved quality of life, greater self-esteem, confidence, and outgoing behavior in their implanted children in comparison to the preimplant stage. Difficulties in social relationships with hearing peers remain, however, because of residual lags in oral communication skills, limited access to communication within group situations, and the attitudes of hearing peers. Parents whose children were either adolescents at implantation or who were implanted against their wishes reported more problems ( Christiansen & Leigh, 2005). In summary, recent studies based on parent perceptions indicate that for deaf students, CIs can help children function better socially, but they do not guarantee age-appropriate socialization experiences with hearing peers.

One observational study involving a small sample of children and adolescents with CIs noted that their interaction with hearing peers tended to be unsuccessful ( Boyd, Knutson, & Dahlstrom, 2000). Acceptance by hearing peers is more likely when deaf children exhibit good spoken communication skills, whether they have implants or not ( Bat-Chava & Deignan, 2001, Leigh & Stinson, 1991; Stinson & Kluwin, 2003). Results of qualitative interviews conducted with deaf children with CIs demonstrate ongoing communication limitations ( Preisler, Tvingstedt, & Ahlstrom, 2005). From information obtained through interviews of young people with CIs, Wheeler, Archbold, Gregory, and Skipp (2007) suggest how intelligible speech may facilitate comfort in situations where sign language is not used. Considering that deaf children are typically placed in mainstream settings, parents, educators, and psychologists continue to be concerned about the social adjustment of deaf children and adolescents, both with and without implants. However, implanted children and adolescents are expected to have better communication skills and more positive psychosocial adjustment compared to their unimplanted peers. It is the goal of this study to assess whether this expectation is warranted.

Behavioral Characteristics of Deaf Adolescents

A review of the literature indicates that the prevalence of negative behaviors in deaf students varies from 4.8% to 50.3% depending on the sample studied and type of behavior ( Schirmer, 2001). Greenberg and Kusche (1989) note that deaf children exhibit greater impulsivity and inadequate emotional regulation compared to hearing children. They believe that this is primarily because of limited exposure to language and social experiences. More recently, Khan, Edwards, and Langdon (2005) found no difference between age-matched groups of hearing children, children with CI, and children with hearing aids on the Child Behavior Checklist. However, sustained attention as measured by the Attention Sustained subscale of the Leiter International Performance Scale-Revised was lower for children with hearing aids in comparison to hearing children and those with implants. Given that direct communication plays an even more important role in the life of adolescents compared to younger children ( Preisler et al., 2005), there is a need to compare the behavior of deaf adolescents with and without implants.

Self-perceptions and Identity of Deaf Adolescents

The self-perceptions of deaf mainstreamed students appear to be generally positive even when they are not accepted by hearing peers (for a review, see Stinson & Kluwin, 2003). Nicholas and Geers (2003) used a picture assessment of self-image in a sample of 181 8- to 9-year-old children who had been implanted four or more years. Results indicated that self-perceptions were positive in most aspects of daily life, thus counteracting earlier concerns about psychosocial problems in implanted children. Whether positive self-perceptions among deaf students continues into adolescence is yet unknown. It is also important to assess how social identity relates to self-perceptions and psychological well-being in adolescents, given that social identity reflects a conscious affiliation with a shared social group, thereby providing stability and continuity in self-perceptions ( Erikson, 1968, 1980).

According to Corker (1996), deaf identity is not necessarily a core identity for deaf children who grow up within hearing families. Instead, incorporating a deaf identity into one's self-image is a developmental task that is influenced by family and school variables. For example, growing up in a family that emphasizes one's deafness as different from hearing family members or peers can strengthen the salience of a deaf identity, whereas growing up in an environment that minimizes such differences may diminish the emphasis on deafness as a core part of one's social identity. In a meta-analytic review of the literature examining factors that influenced self-esteem in deaf adolescents and adults, Bat-Chava (1993) found five studies that assessed the relationship between cultural deaf identity and self-esteem. Having a stronger deaf identity contributed positively to self-esteem in deaf adolescents and adults.

Christiansen and Leigh (2005) were among the earlier explorers of the relationship between CI usage and deaf identity development, having conducted a small interview study of implanted older adolescents and young adults, some of whom had stopped using the CI. Almost all those interviewed desired ongoing contact with the deaf community in addition to socializing with hearing peers. Wheeler, Archbold, Gregory, and Skipp (2007) later administered a semi-structured questionnaire to 29 young adolescents aged 13-16 with CI in mainstreamed and specialized educational settings. They found that most participants were flexible in terms of communication mode and endorsed a perception of deaf identity that was neither culturally Deaf nor strongly hearing. There was no clear relationship between the educational setting students attended and their identity status. Whether this flexibility continues into later years remains to be seen, as the participants were in the younger phase of adolescence.

Borrowing from racial identity development theories (e.g., Helms, 1990), Glickman (1996) proposed that deaf people evolve through four stages of identity development, from first internalizing the need to be like hearing people (hearing identity), to confusion about where one belongs (marginality), to an immersion stage (characterized by denigrating hearing values), and ultimately to a bicultural identity (integrating both hearing and deaf ways of being). Glickman acknowledges that the process is not necessarily linear. In a study of eight adolescents with CI and 37 adolescents without CI that used Glickman's (1996) Deaf Identity Development Scale, mean item scores indicated that hearing identity was more frequently endorsed by participants with a CI compared to participants without a CI ( Wald & Knutsen, 2000). The groups were more similar than different in the bicultural, immersion, and marginal categories, with the bicultural identity receiving the strongest endorsement.

Maxwell and Zea (1998) proposed that many deaf people negotiate their involvement in both deaf and hearing cultures. Borrowing from acculturation research (e.g., Birman, 1994; Landrine & Klonoff, 1994; Zea et al., 2003), Maxwell and Zea investigated how the various domains of acculturation (e.g., psychological identification, cultural/linguistic competence, cultural behaviors/involvement) are expressed among deaf individuals. In a subsequent study involving over 3,000 deaf and hard-of-hearing adults, Maxwell-McCaw (2001) found that those who were deaf acculturated or bicultural had a more positive sense of overall well-being than those who were hearing acculturated, suggesting that some degree of affiliation with the Deaf community facilitated psychological well-being. Marginalized individuals had the lowest levels of psychological well-being. These findings were supported by a follow-up study in Germany, with bicultural respondents being most advantaged psychosocially ( Hintermair, 2008). Neither Maxwell-McCaw nor Hintermair specifically examined whether cochlear implantation had a mediating effect on these results. The current study attempts to build on previous research and examine the effect of cochlear implantation on the relationship between group identity and psychosocial well-being.

Purpose of the Study

The purpose of this investigation was threefold. First, we wanted to expand the knowledge base on the psychosocial effects of CI by focusing on a group that has received little attention in the literature: deaf adolescents. To do so, we compared the experience of adolescents with implants to those without implants. Second, we wanted to go beyond the previous research focus on parents' perspectives. Specifically, we collected self-report data from adolescents themselves, as well as data from parents and teachers with the goal of using multiple sources of data to assess the effect of implants on the psychosocial functioning of deaf children. Finally, because there has been relatively little research on this topic, our goal was to explore the associations among multiple variables in a small sample to inform future research. Because of the dearth of research on the psychosocial adjustment of deaf adolescents with CI ( Marschark, 2007; Wheeler, et al., 2007), we have cast a wide net in an effort to narrow down the factors that are likely to be important indicators of academic and social functioning.

Method

Participants

Fifty-seven deaf high school students, ranging in age from 13 to 18 ( M = 15.79, standard deviation [ SD] = 1.35), participated in this study. Twenty-eight (49%) were CI users, and 29 (51%) had never used an implant. Sixty percent of the participants were female ( N = 34). Of the 55 adolescents who reported race/ethnicity, the vast majority was white ( n = 46, 84%). Of the parents who reported their own level of education, the majority was highly educated: 24 (45%) had either 2- or 4-year college education and 22 (42%) had a postgraduate degree.

For CI users, the average age of implantation was 7 years ( M = 7.33, SD = 3.61) and the average length of use was 8 years ( M = 8.37, SD = 3.34). There were no significant differences between implant users and nonusers in terms of age, ethnicity, gender, age at diagnosis of hearing loss, and parental level of education. Participants also did not differ in reading ability (7.3 grade equivalent for the CI users and 6.5 for the nonCI users). One parent (4%) of a participant with a CI was hard of hearing; the remaining 27 sets of parents were hearing. In the nonimplant group, 9 out of 29 (31%) participants had deaf parents. The two groups differed significantly in educational placements: 26 (93%) of implant users attended mainstream settings (either full or partial), and 15 (52%) of nonusers attended mainstream settings, χ ² ( N = 57; degrees of freedom [ df] = 1) = 11.94, p = .001. In addition, despite attempts to recruit adolescents with comparable levels of hearing loss (before the implant), the two groups differed on this variable. Of the adolescents with CI whose parents reported this information, 25 (96%) had a profound hearing loss and 1 (4%) had a severe hearing loss. Of those without implants, 16 (67%) had a profound hearing loss, 7 (29%) had a severe hearing loss, and 1 (4%) had a moderate hearing loss, χ ² ( N = 50; df = 2) = 7.41, p = .025. The two groups also differed in preferred mode of communication. The majority of implant users (77%) preferred to use oral communication exclusively, whereas only 15% of non-users preferred oral communication. Nonusers reported preference for American Sign Language (ASL) only (26%), Total Communication/Signing Exact English (26%), and code-switching (26%), χ ² ( N = 53; df = 5) = 21.53, p = .001.

Instruments

Adolescents.

Acculturation was assessed using a 30-item version of the Deaf Acculturation Scale (DAS; Maxwell-McCaw, 2001). This version consists of two acculturation scales, one measuring acculturation to Deaf culture and the other measuring acculturation to Hearing culture. Each scale measures acculturation across five dimensions: (1) psychological identification (e.g., "I feel that I am part of the Deaf community"), (2) cultural involvement (e.g., "How much do you enjoy socializing with hearing people?"), (3) cultural preferences (e.g., "I would prefer my best friend(s) to be deaf"), (4) cultural knowledge (e.g., "How well do you know important events in American/World history?"), and (5) language competence (e.g., "How well do you sign using American Sign Language (ASL)?". Items are scored on a 5-point Likert scale from 1 (e.g., "strongly disagree" or "not at all") to 5 (e.g., "strongly agree" or "excellent/like a native"). Subscale scores were averaged, culminating in one score for Deaf acculturation (DASd) and one for hearing acculturation (DASh). Cronbach alphas for the DASd subscales ranged from .83 to .96 and for the DASh subscales ranged from .68 to .87.

Self-esteem was assessed using the Self-Perception Profile for Adolescents ( Harter, 1988). Respondents identify which of two statements apply to them (e.g., "Some teenagers find it hard to make friends" or "Other teenagers find it's pretty easy") and then rate the statement as "Really true for me" or "Sort of true for me." There are nine scales of five items each, culminating in 45 items, scored on a scale from 1 to 4, with higher scores representing more positive self-perception. We report on three subscales that address self-perception in academic and social functioning as well as global self-perception because these reflect the factors most relevant to the purpose of this study. Cronbach's alphas ranged from .67 (global self-esteem) to .77 (scholastic competence and close friendships).

Satisfaction with Life was assessed using the brief Satisfaction with Life Scale ( Diener, Emmons, Larsen, & Griffin, 1985). This scale consists of five items measuring global life satisfaction, such as "I am satisfied with my life." Items are scored on a 7-point scale from 1 (strongly disagree) to 7 (strongly agree). Cronbach's alpha was .80.

Loneliness was assessed using the four-item version of the Revised UCLA Loneliness Scale ( Russell, Peplau, & Ferguson, 1978; Russell, Peplau, & Cutrona, 1980), a summed rating scale consisting of two positively worded and two negatively worded items (e.g., "No one really knows me well"). Responses are given on a 4-point scale from 1 ("never") to 4 ("often"). In this study, Cronbach's alpha was .59. At least one other study found a modest internal reliability (.63) for this scale ( Hays & DiMatteo, 1985).

Student demographic form was used to collect demographic information as well as deafness-related variables (e.g., age at which hearing loss was detected, educational setting), and psychological variables not otherwise measured (e.g., teacher support as perceived by the student, satisfaction with communication at school, with family members, and with peers).

Adolescents with implants were also asked about their perceived acceptance by deaf and hearing peers (from 1 "always" to 4 "almost never") and their patterns of socializing with deaf and hearing peers (from 1 "all the time" to 4 "very little/never"). Acceptance and socializing were very highly correlated ( r hearing = .59, p = .001; r deaf = .41, p = .039) and were combined into acceptance/socialization by calculating the mean of the two items. Items measuring perceived acceptance and socialization were inadvertently left off the questionnaires for nonusers.

Reading comprehension was assessed by the Reading Comprehension Subtest of the Peabody Individual Achievement Test-Revised ( Markwardt, 1998). For each two-page item, the student reads a sentence silently and on the next page selects one of four pictures that best illustrates the sentence.

Parents.

Adolescents' psychosocial functioning was assessed with the Achenbach System of Empirically Based Assessment (ASEBA; formerly called the Child Behavior Checklist, CBCL), Parent Form (Achenbach & Rescorla, 2001). Parents report on their child's (ages 6-18) problem behavior and adaptive functioning (competence), identifying the extent to which "in the past 6 months" each item is "not true," "somewhat or sometimes true," or "very true or often true." The ASEBA has previously been used effectively with children and adolescents who are deaf and hard of hearing (e.g., Wallander, Varni, Babani, Banis, & Wilcoz, 1988). In this study, we used five subscales from the problem behavior section and two competence subscales, selected for their relevance to academic functioning (e.g., attention problems) or social functioning (e.g., rule-breaking behavior, social competence). Cronbach's alphas for the problem-behavior scales ranged from .76 (rule-breaking behaviors) to .86 (Attention problems). Alphas for the competence scales were .56 (social competence) and .58 (school competence).

Parent demographic form

Parents were asked some of the same questions as the adolescents in order to both corroborate the information provided by the student as well as to assess major areas of differences between child and parent. Parents were also asked for demographic information that their adolescents may not have known, such as degree of hearing loss, etiology, and age at diagnosis. Other items assessed parents' education and occupation, ethnic/racial information, and household composition.

Teachers.

Teacher perceptions

Adolescents' psychosocial functioning was assessed by the adolescents' teachers using the ASEBA (formerly called the CBCL), Teacher Form (Achenbach & Rescorla, 2001). This measure covers functioning in two areas, academic achievement and social functioning. Scales are identical to those on the Parent Form. We used the same five subscales from the problem behavior section as used on the Parent form. Cronbach's alphas ranged from .61 (rule-breaking behaviors) to .91 (Attention problems).

Procedure

Participants were recruited through organizations serving deaf individuals and their families, online discussion groups for parents of deaf children and word of mouth. Interested parents and adolescents contacted one of the researchers to set up interviews. Two of the researchers traveled throughout the United States for data collection. Informed consent was obtained from parents and adolescents at the beginning of the scheduled appointment. Researchers communicated with the adolescents in their preferred mode of communication (spoken English, signed English, or ASL). Measures were presented in the same order. The typical length of time required for administration of all adolescent measures was about an hour. The ASEBA Teacher Form was mailed or given to teachers selected by the adolescents.

In addition, two schools for the deaf2 sent information about the study and parent consent forms to parents of high school students. Following receipt of completed consent forms mailed by the parents, adolescents were contacted on site to give their own consent to participate in the study, fill out measures, and provide teacher names for the Teacher Form, which was then delivered to the teachers to be filled out and mailed to the researchers. The parents of these adolescents were mailed the parent forms to be filled out and mailed back. All parents returned their forms. Participating adolescents were paid $10 and their parents were paid $5.

Results

Comparing Adolescents' and Parents' Responses

Responses to demographic and deaf-related variables that were reported by both adolescents and their parents are presented in Table 1, along with descriptive data (means and SD s for continuous variables or number of responses for categorical variables), the level of agreement between the two sources, and the specific source's information ultimately selected for use in the study. Agreement was calculated either as a percent agreement (when data were categorical) or as a correlation coefficient (when data were continuous). For most variables, concordance rates were high. In cases where the information pertained to an adolescent's subjective experience (e.g., acceptance by peers, perceived teacher report), the adolescent's report was selected for subsequent analyses. When the information was factual, the source selected was the one who seemed more reliable (e.g., fewer missing data).

Differences Between Adolescents With and Without CI

Despite our efforts to recruit adolescents with similar characteristics into the two groups, there were significant differences between the groups in type of schools attended and degree of hearing loss (see Methods). To adjust for these differences, we compared the two groups on all outcome variables using a series of one-way analysis of variance with school setting and degree of hearing loss as covariates. Although school setting is not a continuous variable, it was coded to represent an ordinal variable from most integrated with hearing students (i.e., full mainstream = 1) to least integrated (i.e., deaf school = 3). Table 2 presents the results of these tests on all outcome variables. After adjusting for the two covariates, the two groups differed from each other significantly on one variable: adolescents with CI scored significantly higher on hearing acculturation relative to those without CI. Approximately 40% of both groups scored as bicultural [χ ² (3, N = 54) = 9.58, p < .05]. Therefore, our results indicate that in this sample, aside from the hearing-Deaf acculturation difference, adolescents with and without implants were quite similar on the variables assessed in this study.

The covariate of school setting seems to play a larger role in psychosocial adjustment than CI status. Compared to those adolescents attending deaf schools, adolescents attending mainstream educational settings had significantly lower scores in deaf acculturation, higher hearing acculturation, and marginally higher levels of scholastic self-esteem, friendship self-esteem, and global self-esteem. They also had significantly higher parent-reported social competence (see Table 2).

The associations between the covariate "Level of hearing loss" and the outcomes were assessed using an F statistic in an analysis of covariance model. Degree of hearing loss was significantly associated with hearing acculturation and with attention problems reported by both parents and teachers and had a weaker (and nonsignificant) association with acculturation to Deaf culture and parent-reported rule breaking behavior (see Table 2).

Associations between deaf-related variables and psychosocial functioning.

Table 3 presents the bivariate correlations of deaf-related variables with psychosocial functioning, reported for the full sample by child, parent, and teacher. An overall view reveals that some deaf-related constructs are related to both child- and parent-reported psychosocial functioning. Teacher-reported functioning is related to a lesser extent to deaf-related variables and often simply reflects parent-reported functioning. The following presentation of the analyses is based on psychosocial functioning outcome variables, both those reported by adolescents themselves and those included in the ASEBA syndrome profile of problems and competence sections reported by parents and teachers.

Psychosocial indicators reported by adolescents.

Deaf acculturation and hearing acculturation

As seen in Table 3, deaf acculturation and hearing acculturation were associated with several of the measured variables and always in the direction opposite of each other. Notably, satisfaction with home communication was positively related to hearing acculturation. Conversely, number of deaf friends was positively associated with deaf acculturation and negatively associated with hearing acculturation. Grade was also related to acculturation such that adolescents in higher grades had higher deaf acculturation scores and lower hearing acculturation scores than those in lower grades.

Self-perceptions

Several self-esteem subscales were associated with the antecedent variables. As indicated in Table 3, self-reported scholastic competence was negatively associated with age when hearing loss was detected, such that those whose hearing loss was detected earlier had higher scholastic self-esteem. Students who were more satisfied with home communication had higher levels of scholastic self-esteem and social competence self-esteem.

Satisfaction with life

Table 3 demonstrates that satisfaction with life was positively related to satisfaction with home communication.

Psychosocial indicators reported by parents/teachers.

Negative functioning

Attention problems (reported by both parents and teachers) were positively associated with age at which hearing loss was detected (later age was related to greater problems), rule-breaking was positively associated with age at detection of hearing loss according to both parent and teacher report, and parent-reported aggression was positively related to age at which hearing loss was detected (Table 3).

Positive functioning

As Table 3 shows, parent-rated social competence was positively related to satisfaction with home communication, whereas satisfaction with communication at school was not associated with any of the indicators of psychosocial functioning. Parent-rated scholastic competence was negatively associated with age at which hearing loss was detected, age, and grade.

Partial correlations adjusting for implant status.

To assess the possible role of CI use on the relationship among the variables, partial correlations or analysis of covariance were calculated among all variables, adjusting for CI status. These associations were generally the same as the bivariate associations reported earlier, indicating that beyond its direct relation with outcomes, CI status did not have a moderating effect on the relations among variables.

Associations Between Deaf-Related Variables and Psychosocial Functioning for Adolescents With CIs

In addition to the correlations computed for the full sample, several correlations were computed for the subsample of adolescents with implants, correlating the same psychosocial functioning variables described above with those variables assessed only for this subsample: level of teacher support and socialization/acceptance by hearing peers and deaf peers. Table 4 presents these bivariate correlations.

Acculturation and self-perceptions.

Similarly to the full sample, deaf acculturation and hearing acculturation were associated with several of the measured variables and were always in the direction opposite of each other. Deaf acculturation was positively related to socialization with and acceptance by deaf peers and negatively related to socialization with and acceptance by hearing peers. Conversely, hearing acculturation was positively related to socialization with and acceptance by hearing peers and negatively related to socialization with and acceptance by deaf peers. Scholastic self-esteem was negatively related to socialization with and acceptance by deaf peers. Satisfaction with life was negatively associated with perceived teacher support. Finally, loneliness was positively associated with socialization with and acceptance by hearing peers.

Psychosocial indicators reported by parents and teachers.

None of the parent-reported variables on negative functioning was associated with any of the social variables assessed for adolescents with implants. Teacher-reported withdrawn behavior was negatively associated with perceived teacher support and with socialization with/acceptance by hearing peers. Parent-reported scholastic competence was positively related to socialization with and acceptance by hearing peers (see Table 4), taking into account the modest reliability of the social and scholastic competence scales on the ASEBA.

Discussion

From a psychosocial perspective, deaf adolescents with a CI and those without the device in our sample are far more similar to each other than might have been anticipated, given the expectations that the CI might make a significant difference in adolescents' social development, even while adjusting for educational settings and degree of hearing loss. Although there are many more similarities than differences between the two groups, our research reveals the presence of some interesting differences and associations. One purpose of this study was to examine the associations among multiple variables investigated in this study in order to inform future research. These differences and future research suggestions are discussed below.

Acculturation

The one notable difference between the two groups is that those with CI are more likely to be acculturated to hearing society than those who do not use the device, whereas non-CI adolescents are more Deaf acculturated than those with CI. It is no surprise that the CI group generally is more hearing acculturated, given that many parents and educators are encouraged to maximize auditory training post-implantation. It is also possible that this group, even considering their greater degree of hearing loss prior to implantation, had higher hearing orientation due to hearing aid use and auditory training prior to implantation. In addition, more of the adolescents without CI were in specialized deaf settings where, despite their relatively lower levels of hearing loss, auditory training perhaps was less emphasized and deaf identities were more likely to be reinforced. All these factors could contribute to the difference in the findings on hearing-Deaf acculturation. However, it is important to emphasize that implant users can be primarily acculturated either to deaf or hearing worlds, or to both (as indicated by the similar number of participants with bicultural identities in both groups), though this CI sample was predominately hearing acculturated. Rather than having a direct effect on the psychosocial outcomes assessed in this study, it seems that CI status exerts an influence over many of this study's outcomes through the mediating effect of acculturation and school setting. Although it appears that hearing acculturation is associated with CI use, this does not necessarily mean that the CI is key. Other mediating variables, such as spoken language competency, also need to be examined. Because this study did not compare differences between hearing-aid users and CI users, future studies that specifically address this variable may shed more light on this issue.

Despite the differences in acculturation, the two groups nonetheless do not significantly differ in the other psychosocial outcomes. This suggests that the differing acculturative experiences between these two groups-alignment with either the majority hearing society or with Deaf culture-do not have a differential effect on the psychosocial outcomes of self-esteem, satisfaction with life and loneliness when reported by adolescents themselves, as well as psychosocial characteristics reported by parents and teachers.

Scholastic Self-esteem, Socialization, and Satisfaction With Life

The negative relationship between scholastic self-esteem and socialization with and acceptance by deaf peers for CI users might be due to subtle parent and school messages that focus on the value of hearing peer socialization and mainstream education rather than deaf peer socialization or specialized deaf settings. The negative association between satisfaction with life and perceived teacher support could be interpreted to mean that those CI users who were satisfied with life required less teacher support than those with lower satisfaction scores.

Loneliness Among Deaf Adolescents

In our study, levels of loneliness were similar for both groups, considering that the CI users were mostly in mainstream settings and adolescents without CI were in mostly specialized settings. Although the reliability of the Loneliness Scale was modest in this study, our findings appear to be consistent with the existing literature. Most (2007) found similar levels of loneliness among young deaf adolescents in inclusion settings and those in specialized classes within a mainstream setting. This finding supports our own findings regarding the similarity of loneliness levels for students in mainstream and specialized schools. In addition, Most (2007) reports a negative correlation between speech intelligibility and loneliness only for the inclusion sample. She suggests that students in the specialized setting may have felt set apart from the mainstream, whereas students in the inclusion setting may have been subject to ongoing communication difficulties and feelings of rejection.

Overall, Most's participants had higher levels of loneliness than a normative hearing sample. However, in a study of the effects of CI experience on loneliness in middle and late childhood, Schorr (2006) found that loneliness levels among her participants were similar to levels noted in hearing peers. In our sample, loneliness levels did not significantly differ from those in the normative sample reported for the Revised UCLA Loneliness Scale (D. Russell, personal communication, December 9, 2007). In alignment with Schorr's (2006) findings, our findings imply that deaf adolescents are not more lonely compared to hearing peers. The positive relationship between loneliness and socialization with and acceptance by hearing peers found for CI users in this study might be explained by the possibility that socialization with hearing peers averts loneliness mainly in one-on-one interactions but not in group situations where loneliness might easily arise. It would be helpful to further examine the factors that might be related to loneliness for adolescents with CI, such as receptive speech comprehension, functional communication skills (particularly in noisy situations), types of social skills, and the availability of deaf peers in mainstream settings. Future studies should use the full Revised UCLA Loneliness Scale and assess its reliability with deaf adolescent populations.

Peer Acceptance

Researchers have noted that parents do not consistently evaluate social situations the same way their children do (e.g., Gregory, Bishop, & Sheldon, 1995). The low correlation between parent and adolescent CI user ratings of acceptance by deaf peers (−.13) suggests the possibility that adolescents may view their acceptance by deaf peers differently than parents do. The moderate correlation for acceptance by hearing peers (.45) could possibly be due to a greater focus by parents on acceptance by hearing peers, which might also influence the desire of adolescents to view themselves as being accepted by hearing peers. These correlations reinforce the importance of asking children as well as parents about their perceptions of peer relationships and loneliness.

The Effect of School Setting on Psychosocial Outcomes

Students attending mainstream settings report higher self-esteem within the domains measured in this study, including scholastic self-esteem. It is impossible to conclude from these data whether adolescents with higher perceived scholastic self-esteem tend to go to mainstream schools or if attending mainstream schools contributes to such positive self-perception.

Our results also indicate that adolescents educated in mainstream settings are more likely to be hearing acculturated compared to those attending deaf schools. Additionally, similar percentages of adolescents with and without CI endorsed a bicultural identity. This appears to support the idea that even in view of the stronger orientation toward hearing acculturation on the part of CI users, it is possible for CI users to have bicultural orientation. Considering the Maxwell-McCaw (2001) and Hintermair (2008) findings of an association between bicultural identity and well-being, the implication is that of a psychosocially healthy process.

The Effects of Age and Grade on Psychosocial Outcomes

Students in higher grades are more Deaf acculturated, and students in lower grades are more hearing acculturated. It is possible that students in higher grades increasingly experience communication difficulties with hearing peers and in the classroom and, as a result, desire a stronger connection with deaf peers or academic environments with critical masses of deaf students. Alternatively, this result could lend support for Glickman's (1993) supposition that as deaf individuals explore their identity more fully, they move toward the immersion stage in order to explore their interest in and comfort within the deaf community. Given that this sample is composed entirely of adolescents and that, according to Erikson (1968, 1980), adolescence is focused on explorations of identity, our finding that adolescents in different grades have different levels of hearing and Deaf acculturation is not surprising. Longitudinal studies following adolescents' identity development through several grades as well as the influence of deaf peer presence in mainstream settings may clarify the extent to which development and exposure to deaf peers influence acculturation choices over time.

The Effect of Hearing Loss levels on Psychosocial Outcomes

Early detection of hearing loss was related to feelings of academic and social competency. Another interesting finding is the association of hearing loss levels with attention problems and rule-breaking behavior. Based on parent and teacher reports, lower levels of hearing loss were associated with greater disruptive behavior. It is possible that when children have greater hearing loss, family members' expectations about their ability to communicate are more realistic than when their hearing loss is less severe. Unrealistic expectations about the child's ability to communicate, in turn, cause family members to report more disruption ( Harvey, 2003; Laszlo, 1994; Pipp-Siegel, Sedey, & Yoshinaga-Itano, 2002). When cues are misunderstood, frustration may increase. It is further possible that those with a more severe hearing loss may have received intervention sooner and more extensively than those with less severe hearing loss ( Kochkin, Luxford, Northern, Mason, & Tharpe, 2007).

The Effect of Home Communication on Psychosocial Outcomes

Based on an analysis of the psychosocial functioning of the entire sample of adolescents with and without CI, the importance of communication at home is demonstrated by findings that satisfaction with home communication was positively related with hearing acculturation, scholastic self-esteem, social competence self-esteem, and satisfaction with life. Considering that early detection of hearing loss was also related to feelings of academic and social competency, this implies that when parents are hearing, earlier intervention leads to greater ease of communication at home, which in turn leads to greater comfort in social and academic environments. Based on a data set of 213 parents in Germany, Hintermair (2006) noted that when the child is communicatively competent, the child's socioemotional development is facilitated and parents experience less stress.

Conclusion

The purpose of this preliminary study was to assess differences between adolescents with and without implants in several areas of psychosocial functioning according to reports by adolescents themselves, their parents and teachers in order to inform future research. In addition, deafness-related antecedents of psychosocial functioning were examined for all adolescents, both those with and without implants. This study is unique in that it examines adolescents with CI, a group not previously studied, and includes data from multiple sources: adolescent themselves, parents, and teachers. Our most notable finding is that deaf adolescents' functioning-as measured in this study-is not directly affected by implant status. CI status exerts an indirect influence on several of the psychosocial outcomes in our study through its effect on acculturation and school setting. Because our small, nonrandom and self-selected sample may have introduced some biases, our findings should be replicated with larger and more representative samples, including samples of children implanted at younger ages than was typically the case in this study. Other types of data, such as objective observations of children and adolescents with implants in naturalistic settings may shed more light on the possible difference relative to those without CI. In addition, a longitudinal design to ascertain cause and effect might be appropriate for answering questions such as the following: Are students who are hearing acculturated choosing to attend mainstream schools, or does attendance in mainstream schools contribute to hearing acculturation? Our observations of the minor differences between parent and teacher reports lead us to suggest that future research could consider omitting teacher reports, which are more difficult to collect. Future research may want to consider measuring other psychosocial as well as academic constructs, which may yield greater differences between adolescent CI users and those who do not have a CI.

Overall, it is apparent from this study that the CI is an assistive tool and not a factor that significantly impacts psychosocial functioning as measured in this study, contrary to what was believed a decade ago. It appears that, considering the entire sample of participants in this study, how parents and the educational system meet the psychosocial needs of these deaf adolescents is of more importance than whether or not the adolescent has a CI.

Funding

Priority Grant from the Gallaudet Research Institute.

The authors wish to thank graduate students Tracy Durham, Brittney Farneth, Caroline Kobek-Pezzarossi, Angelia Lawson, Cara Miller, and Daniel Nead, as well as Sue Hotto of the Gallaudet Research Institute and Daniel Russell, PhD, of Iowa State University for their assistance with different portions of this study.

No conflicts of interest were reported.

References

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Correlates of Psychosocial Adjustment in Deaf Adolescents With and Without Cochlear Implants: A Preliminary Investigation | The Journal of Deaf Studies and Deaf Education | Oxford Academic

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Adolescence is characterized by rapid physiological, psychological, and social development (e.g., Harter, 2003; Kroger, 1996; Noller, Feeney, & Peterson, 2001). Deaf adolescents undergoing these changes also typically confront the challenges of being deaf in a sound-dominated environment not always attuned to their auditory and visual needs, particularly in the school setting.

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Eval Phon process

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Abstract

For many years, people who are stakeholders in the task of fostering the communication skills of children with severe-to-profound hearing loss have described the relationship between intervention, language, and literacy outcomes. This descriptive research describes generally low achievement levels across communication skills including speech intelligibility, spoken English, and written English comprehension. In particular, children born with profound hearing loss typically graduate from high school reading at the fourth-grade level, given standardized assessments ( Goetzinger & Rousey, 1957; Pinter & Patterson, 1916, 1917; Traxler, 2000). According to Ogle et al. (2003), the type of reading skills needed to perform at mean benchmark competencies for a fourth-grade reading level include the ability to (a) make elementary inferences, (b) locate specific parts of a text to retrieve information, and (c) make observations about whole texts.

On the other hand, functional reading literacy is defined as the ability to understand, use, and reflect on written texts in order to achieve one's goals, to develop one's knowledge and potential, and to participate effectively in society ( Boudard & Jones, 2003). The skills involved in functional reading literacy include the following: (a) reading lengthy, complex, abstract prose texts as well as synthesizing information and making complex inferences, (b) integrating, synthesizing, and analyzing multiple pieces of information located in complex documents, and (c) locating more abstract quantitative information and using it to solve multistep problems when the arithmetic operations are not easily inferred and the problems are more complex ( Kutner et al., 2007).

As such, we can conjecture that in the United States, current technological and societal demands necessitate a 10th- or 11th-grade reading ability for functional participation in society ( Marschark & Harris, 1996). Allen (1986) estimated that more than 30% of deaf children graduate from high school functionally illiterate.

There is evidence that some outcomes with regard to reading skills of some deaf children are changing, however. Studies of children who receive cochlear implants (CIs) reveal that they gain access to spoken language. This access is also associated with higher levels of speech intelligibility, better vocabulary skills, and better language skills than their peers with profound hearing loss who do not wear CIs (e.g., Geers, Nicholas, & Sedey, 2003; Peng, Spencer, & Tomblin, 2004; Vermeulen, Hoekstra, Brokx, & van den Broek, 1999). On the premise that reading skills are influenced by spoken language skills, investigators have likewise begun to document increased reading comprehension at the word and paragraph level in children who use CIs ( Connor & Zwolan, 2004; Geers, 2003; Spencer, Barker, & Tomblin, 2003; Spencer, Tomblin, & Gantz, 1997).

In hearing children, we know that an intact phonological system provides an important foundation for learning to read ( Nathan, Stackhouse, Goulandris, & Snowling, 2004; Rayner, Foorman, Perfetti, Pesetsky, & Seidenberg, 2001); however, little is known about the phonological skills of prelingually deaf children who use CIs. Phonological Awareness (PA) is defined as the ability to abstract and manipulate segments of spoken language ( Bentin, 1992; Liberman, Shankweiler, Fischer, & Carter, 1974; Mattingly, 1972). In brief, the construct of PA is a composite of several abilities. Three distinct levels of PA are proposed: breaking individual words into smaller units from words to syllables, then into the onset-rime level, and finally into the phoneme level ( Bradley & Bryant, 1991; Perfetti, Beck, Bell, & Hughes, 1987; Stahl & Murray, 1994). PA is part of a broader construct, PP, which incorporates phonological memory.

A final, albeit controversial skill possibly involved with Phonological Processing (PP) is rapid naming, which involves retrieving sound information from long-term (permanent) memory to name pictures, letters, or numbers. Wagner, Torgesen, and Rashotte (1999) proposed that young readers first retrieve sounds associated with letters or letter pairs, then they retrieve the pronunciations of common word segments, and finally they retrieve the whole word. In this view, rapid naming is predictive of early reading skill. This is because rapid naming measures the efficiency of retrieving phonological codes associated with phonemes, word parts, or entire words ( Shankweiler & Crain, 1986; Share, 1995; Torgesen & Burgess, 1998). Rapid naming predicts reading skills; yet, the nature of its relationship to assessment of phonological skills is unclear. Wagner and Torgesen (1987) originally believed that rapid naming was a phonologically based skill. Subsequently, researchers have proposed that rapid naming is a measure of several skills including PP and executive functioning ( Denckla & Cutting, 1999), and/or the ability to detect and represent orthographic redundancy ( Bowers & Wolf, 1993; Wolf, 1999). Alternatively, rapid naming has been proposed to measure global processing efficiency ( Kail, Hall, & Caskey, 1999) and is an index of attention skill ( Neuhaus, Foorman, Francis, & Carlson, 2001).

In this article, the term PP indicates the broader, more encompassing construct that includes a phonological memory and naming component. There are numerous tasks that can be used to assess PP, but there are four common operations used during assessment procedures (McBride-Chang, 1995). The operations include (a) listening and perceiving the words that are typically presented in an oral modality; (b) holding the phonological representation in memory; (c) performing a manipulation on the speech segment (e.g., deletion, identification); and (d) communicating the result of the operation they performed, usually with a spoken response.

Valid assessment of PA or PP skills of children with profound hearing loss is challenging because their performance on assessment tasks is confounded by their ability to listen to and hear the test items and then to produce accurate spoken responses. Furthermore, it is often assumed that these PP skills are not well developed in children with hearing loss. Consequently, in many cases, explicit reading instruction does not build on or emphasize those skills. Thus, teachers of the deaf and hard of hearing (D/HH) have tended used basal reader approaches that favor "whole word" reading (Webster, 2000).

Instructional methods for children with profound hearing loss have been criticized in the past for failure to incorporate phonological or "sound-based" reading strategies into the reading curriculum ( Hanson, 1989; Nielsen & Luetke-Stahlman, 2002). LaSasso and Mobley (1997) surveyed reading instructional methods and materials used by teachers of the deaf and found that very few teachers used sound-based (phonological) reading strategies. Furthermore, only 22% of teachers rated their knowledge of reading theory as up-to-date, and only 24% rated their knowledge of instructional strategies and variables that affect reading development as current. Schirmer and McGough (2005) noted that there was little research documenting the effectiveness of instructional interventions in deaf children, particularly using the topic of phonics instruction.

There is, however, indirect support for using sound-based reading instruction with deaf learners. Skilled reading among deaf readers who do not use CIs is predicted by knowledge of print-to-sound correspondence, speech intelligibility, speech reading skills, and ability to extract phonological information from print ( Hanson, 1982; Hanson & Lichtenstein, 1990). Two recent studies have provided direct support for using sound-based reading instruction for children who are D/HH. Trezek and Malmgren (2005) evaluated a phonics treatment package that was used with middle school-aged children who were D/HH. Results of this study demonstrated a treatment effect of phonemic awareness instruction. Additionally, Trezek and Wang (2006) found that D/HH clients who received 1 year of a phonics-based curriculum that was supplemented by Visual Phonics (which is a system that combines written and hand signs to augment grapheme-to-phoneme teaching) demonstrated improvements in reading skills. The reading improvement was documented by statistically higher scores on standardized tests of word reading, pseudoword decoding, and reading comprehension after they received intervention with the Visual Phonics treatment program.

The widespread use of CIs in children provides additional justification for incorporating more sound-based instructional strategies into the reading curriculum for children born with profound hearing loss. Researchers have documented that children with congenital, bilateral, profound loss who use CIs develop better speech perception ( Boothroyd & Eran, 1997; Geers & Brenner, 1994; McKinley & Warren, 2000; Vermeulen et al., 1997) and speech production skills ( Geers & Tobey, 1992; Peng et al., 2004; Tobey, Geers, & Brenner, 1994; Tye-Murray, Spencer, & Woodworth, 1995) than their profoundly deaf peers who wear hearing aids. Many CI users also attain higher levels of language and reading comprehension than their peers with profound hearing loss who do not use CIs ( Spencer, Tye-Murray, & Tomblin, 1998; Spencer et al., 2003; Spencer et al., 1997; Tomblin, Spencer, Flock, Tyler, & Gantz, 1999).

Additionally, a recent study (Spencer & Oleson, 2008) found that early speech perception and production skills predicted later reading skills in prelingually deaf children who used CIs. Findings such as these indicate that children with CIs may well develop a more intact phonology, their phonologies may be more testable, and they make more use of their phonology for reading. The limited extant literature suggests that these children develop phonological systems that are stronger than those of their deaf peers without CIs but weaker than those of their hearing peers.

It is important to understand the way children with CIs develop PP skills because of the association between PP and reading achievement in hearing children. One obstacle in assessing these skills in children who use CIs is that there are no standardized testing instruments. To date, there is no valid assessment battery that can potentially identify those CI users who are significantly below their peers with respect to their phonological abilities. If such a battery existed, clinicians could subsequently use PP test information to identify the strengths and weakness regarding the phonological skill development of their clients. The information derived from this testing could, in turn, have implications for remediation of speech, language, and literacy skills, and could guide clinicians and teachers in choosing goals for rehabilitation.

One goal of this article was to investigate whether it was possible to establish the validity of a series of tasks to measure the PA and PP skills of CI users. A second goal of the study was to document the range of PA skills in children with more than 3 years of CI experience and to compare this range with peers who have hearing, but are matched with regard to word-reading level. The final goal of the study was to examine in a preliminary way the relationship between PP skills and reading skills.

Method

Participants

CI users.

Twenty-nine children with CIs participated in this study. They met the following criteria: prelingual, bilateral hearing loss with no other identified cognitive or learning disability; they received a CI at the University of Iowa Hospital and Clinics before the age of 7; they had at least 3 years of CI experience when examined, and they were younger than 18 years. Participants were paid $15.00 for their participation. The average age of implantation was 3 years 7 months ( SD = 2 years 5 months), with an age range of 1 year 6 months to 10 years 8 months. The average age at testing was 11 years 9 months ( SD = 3 years 6 months), with an age range between 7 years 2 months and 17 years 8 months. Etiologies of deafness included nonspecified heredity component, identified GJBT mutation (connexin 26), meningitis, cytomegalovirus infection, cochlear malformation, Usher Type 1, complications from receiving ototoxic drugs, and unknown etiology. All CI users were educated in public school systems (21 within the state of Iowa, 1 in North Dakota, 1 in Illinois, 2 in Wisconsin, and 3 in Missouri). Parental report indicated that 28 participants were educated using a Total Communication (TC) philosophy. For the purpose of this article, "TC" indicates that the educational programs employed "the combined use of aural, manual and oral modalities in communicating with and teaching hearing impaired individuals" ( Garretson, 1976). One child used an Auditory-Verbal approach to aural habilitation but was educated in a mainstream public school setting where the educators were using a TC philosophy.

All participants underwent CI surgery at the University of Iowa Hospitals and Clinics. The type of CI processors used by the participants included 2 Nucleus 22-channel Spectras, 9 Nucleus 22-channel Sprints, 5 Nucleus 22-channel Esprits, and 12 Nucleus 3Gs. Processing strategies included the following: 22 Nucleus ACE strategies, 1 MPEAK strategy, and 6 SPEAK strategies. Demographic information for the CI participants is given in Appendix A.

Hearing controls.

Thirty-two children with hearing (hearing controls [HC]) were recruited from two area education agencies (AEAs) within Iowa, including the Grant Wood AEA and the Mississippi Bend AEA. These children were recruited to be matched with the CI group on mother's education and word comprehension ability. We performed group matching as a way to experimentally control the variance of performance and to remove age effects. Thus, we matched the groups with respect to word comprehension level rather than for chronological age. This allowed us to assess skills that are associated with relative reading ability, rather than age-related abilities. The average age at testing was 9 years 7 months ( SD = 2 years 8 months), with an age range between 6 years 2 months and 17 years 9 months.

Participants met the following criteria: they had no known hearing loss as noted by passing a hearing screening by their local AEA; they had no identified cognitive or learning disabilities as per parent report; and they had not repeated a grade. Participants were paid $15.00 for their participation. Twenty-nine hearing participants were matched with the CI users according to the mother's education and to their word comprehension grade levels. A t test revealed no significant difference in the mother's education measure between the CI group ( M = 4.24, SD = 0.95) and the hearing group ( M = 4.41, SD = 1.05), t(28) = −.65, p = .52. There was no significant word comprehension grade equivalency difference between the CI group on the Woodcock Reading Mastery Tests Revised Form (WRMT; Woodcock, 1987) ( M = 5.21, SD = 4.01) and the hearing group ( M = 5.15, SD = 4.01), t(28) = .06, p = .95.

The average age of the HC group was nearly 2 years younger than the CI users and was 9 years 7 months ( SD = 2 years 8 months), with an age range between 6 years 2 months and 17 years 9 months. Given the age difference between the two groups, it is important to realize that this study is examining the skills related to relative ability levels, not the skills of a particular age-group. Future studies could investigate skill differences between CI children and their age-mates with hearing. Demographic information for the hearing children is given in Appendix B. All participants were tested individually, in a quiet room.

Rationale for Choosing Tasks as Test Measures

Many children with profound hearing loss are receiving CIs, which provide considerable access to auditory stimuli, and in turn, gains are seen with speech production ability. Thus, phonological skills for speaking and listening are substantially affected. In concert with improved speaking and listening skills, we would expect to see improved PP skills in these children, which should be important for reading. Measuring PP skills in children who are D/HH or who wear CIs can be affected by the child's listening and possibly speech production ability. Therefore, we need to examine the validity of PP measures in CI children.

One of the purposes of the first portion of the study was to produce a set of tasks to obtain a valid measure PP skills of CI users. To do this, we wanted to minimize the influence of hearing on performance. It is hypothesized that if poorer hearing in the CI children is influencing their PP performance, then their performance on parallel tasks that vary in auditory demands will be effected more than what we would find in hearing children with comparable reading abilities. Also, it is hypothesized that if hearing levels affect performance, then the correlations between these parallel tests will differ between the CI and HC children. With this in mind, we wanted to assess whether using audition only was a valid way to measure PP skills in children with CIs, so we had to determine whether performance was related to decreased PP skills or to an inability to perceive the stimuli using auditory-only (A/O) condition. Appendix C contains a summary table of the tasks used.

PP tasks-PA.

The Comprehensive Test of Phonological Processing (CTOPP; Wagner, Torgesen, & Rashotte, 2001) was adapted and administered to both the CI and the HC children. The CTOPP was designed for hearing children and took about 30 min to administer. It had six subtests that assessed PA, phonological memory, and rapid naming. PA was tested by elision and blending tasks. The Elision subtest required the child to listen to the examiner and delete a sound from the stimulus word. Responses were scored as either correct or incorrect. The Blending Words subtest of the CTOPP required the child to listen to stimuli presented on an audio file of a CD played through a personal computer. The protocol involved a cue sentence, "What words do these sounds make?" followed by the test item "can dee." The child was then supposed to put the word together and say "candy." Responses were scored as either correct or incorrect. All raw scores were used to derive a standard score and a grade equivalency score.

A third PA task was administered via a rhyme test that was adapted from James et al. (2005). This task contained 24 trials that were presented on a personal computer and E-Run software ( E-Prime, 2005). Each trial comprised four photos presented on the computer screen (a cue, a target, and two distracters). The choices for the cues contained a target (which rhymed with the cue item) and two distracters that were chosen to have either a semantic relationship or a phonological relationship with the cue. For example, the cue hair had the target pear, with a distracter of bow (semantically related) and hill. Alternatively, the cue wall had the target ball, with the distracter of tie and wig (which started with the same phoneme). Half of the targets were either orthographically congruent (sock, clock) or orthographically incongruent (fruit, boot) to the cue. The cue was the photo on the top of the computer screen. The target and the two distracters were the three photos just under the cue photo. All the photos remained on the computer display. The examiner named each picture on the screen and the child was to pick the photo that rhymed with the cue photo. If a child missed more than 10% of the items (three or more), a vocabulary verification procedure was performed where the child identified the picture that represented a missed vocabulary item from a field of three. Ten CI children had to perform the verification procedure, and three of the HC children performed the verification procedure. All did so with 100% accuracy.

PP tasks-phonological memory.

All participants completed two versions of a Digit Repetition task. The first was the Memory for Digits subtest of the CTOPP. Stimuli were presented using the audio files of the CD provided by the test designers played through a personal computer and external speakers. Responses were scored as per the test directions; the item was correct if the child repeated all digits correctly in order.

A second digit task, Digit Recall was administered. This task simultaneously presented both an auditory and a visual digit stimuli. The task was an adaptive procedure presented via a personal computer and E-Run software ( E-Prime, 2005). The audio file for each single digit (one to nine) was pulled from the CTOPP CD and stored as separate files extracted by AdobeAudition, version 1.5 software ( AdobeAudition, 2004). The audio file for each digit was randomly pulled and paired with a visual presentation of the same digit. The combined visual and auditory stimuli were simultaneously presented via a personal computer. Three digits were presented, one at a time on the computer screen, for example (6), then (5), then (1) paired with the audio file for each digit. The computer screen then went blank and the child was asked to enter the three digits using the number keypad in the correct order and press the enter key. The E-Prime program presented blocks of digits in an adaptive procedure, such that the child had to achieve a criterion of two of the four correct repetitions at each digit level (series of three, series of four, series of five) before the program advanced to the next series level. For example, once the participant was successful with two of four sets of three digits, the program would advance to presentations of four digits, and so on. If the participant was unable to achieve two accurate trials at a particular level, the program would adapt to the previous level and terminated when the participant could not achieve the criterion of two repetitions at a series level. The program recorded the total number of correct repetitions for this task.

The final phonological memory task was the Nonword Repetition subtest of the CTOPP. The protocol involved a cue, "Say ..." followed by the test item, for example, "joop." The child was then supposed to repeat the nonword. Responses were either scored as correct or incorrect. The raw score was used to derive a standard score and grade equivalency.

PP tasks-rapid naming.

Two subtests from the CTOPP were administered, including Rapid Letter Naming and Rapid Number Naming. Each child looked at a series of letters or numbers on a page and was asked to call out the names of the letters or numbers as fast as possible. The time it took the child to name the whole series of letters or the whole series of numbers was recorded. There were two series each of the letters and numbers. The child's raw score was the total time it took to name two series of letters and two series of numbers. The raw score was used to compute a standard score and an age equivalency.

Administration Issues

Two tasks from the CTOPP (Blending Words and Nonword Repetition) required the participants to listen to a prerecorded CD and then make an oral response. This presented a particular challenge to validity with respect to the CI children. A/O presentation for the children with CIs might not be a valid measure of PP because the children might require auditory and visual input of stimuli. In order to rule out that an incorrect response was due to a lack of the PP skill, rather than an inability to hear or receive the stimuli, the following modifications were implemented. At the beginning of the test session, the items for the Blending Words subtest were administered to the children with CIs as whole words using a live-voice, open-set, A/O verification procedure. Thus, an item (e.g., "can-dee") was presented as a whole word "candy." Each child was asked to identify the word to be sure he or she could indeed hear the word in an open-set condition. Twenty-eight of the 29 children with CIs achieved 100% correct on this pretest. The one child who could not complete open-set verification for the task was given spondee words that were identified correctly during speech perception testing as a substitute items in the Blending Words subtest. In order to avoid a possible priming effect, the verification task was completed approximately 1 hr before the Blending Words subtest, and the children were busy during the interim completing all other testing. Second, for all children, if an item was missed on the standard administration of the Blending Words or the Nonword Repetition subtest, the item was readministered at the end of the test session using and audio-visual, live-voice format. Thus, two scores were derived for these two subtests. One score based on A/O presentation and the other based on auditory-visual (A/V) presentation.

Nonverbal Reasoning Tasks

To address the question of whether any results were an artifact related to the child's intelligence level, the children completed two subtests (Nonverbal Memory and Block Design) from the Universal Nonverbal Intelligence Test (UNIT) (Bracken & McCallum, 1998) to yield a brief measure of nonverbal reasoning skills. Test scores were recorded in terms of standard score in order to investigate the contribution of intelligence on the variability of results.

Reading Skills

The Word Comprehension and Word Attack subtests from the WRMT (Woodcock, 1987) were used to measure reading skills. Both subtests were administered according to the procedure outlined in the test administration manual. We completed group-wise matching according to grade-level performance on the Word Comprehension subtest. The Word Comprehension subtest consisted of three tasks that required fairly well-developed vocabulary knowledge. First, the child read a word and supplied a word that meant the opposite of the word read. Second, the child read a word and supplied a synonym for the word. The final task required the child to read a series of words to complete an analogy (big is to small as sweet is to ___). All tasks continued until the child achieved a ceiling score (five incorrect responses). As such, the task was complex and did incorporate some of the decoding skills fond in the subsequent subtest. The Word Attack subtest assesses a child's ability to pronounce orthographic strings. This subtest required the child to read a pseudoword. If the child pronounced the word correctly, credit was given. Raw scores were then translated into standard scores. We used the norms provided from the test to convert the raw score to a standard score based on the child's grade in school.

Socioeconomic Status

Socioeconomic status (SES) has been demonstrated to be associated with reading outcome in children with CIs and those without ( Connor & Zwolan, 2004; Hart & Risley, 1995; Hoffmeister, 1996; Huttenlocher, Haight, Bryk, Seltzer, & Lyons, 1991). Unless a family is in extreme poverty, mother's educational level seems to be the best predictor of the parenting skills that are believed to contribute to language and literacy development ( Bornstein, Hahn, Suwalsky, & Haynes, 2003; Hoffmeister, 1996). For this reason, we used the mother's educational level as measure of SES in order to account for the amount of variance in skill that was accounted for by SES ( Hoffmeister, 1996). SES information for each participant was gathered by determining the highest level of education of the mother of each participant using the following scale: 1 = completed grades kindergarten through grade 8, 2 = completed grades 9-12, 3 = graduated from high school, 4 = completed some post high school programming, 5 = completed a 4 year-college degree, 6 = completed a post-graduate college degree.

Results

Summary Statistics for the PP Tasks

Phonological awareness.

Figure 1 presents the mean grade equivalency scores for the PA tasks, including the Elision and Blending Words in the A/O and A/V conditions. Grade equivalency scores are independent of the age of the child and are based on the raw score. Grade equivalency scores represent the grade level at which the raw score is most likely to occur in a normative population. Because the ages of the two groups were disparate, we chose to represent the data using grade equivalency scores, a reflection of absolute ability rather than the standard score, a measure of relative ability among age-mates of the child.

The Rhyme Task was not part of the CTOPP and therefore did not have normative information available (right-hand side of panel). Therefore, the data are presented as mean raw scores where a maximum possible score was 24. Figure 1 reveals that the mean score for the children in the CI group was lower than that for the children in the HC group on all the PA tasks. For the Elision task, however, there was no significant difference in means-CI group: M = 5.06, SE = 0.77, and HC group: M = 6.03, SE = 0.71, t(56) = −.92, p = .37. For the Rhyme Task, there was a significant difference in means-CI group: M = 21.07, SE = 0.79, and HC group: M = 23.28, SD = 4.03, t(40.2) = −2.51, p = .02 (Satterthwaite correction for heterogeneity of variance used). Also note that the mean scores on rhyme for both groups reflected accuracy levels that were more than 87% on the test.

Effect of A/O and A/V conditions in blending words.

To examine whether there was an effect of task condition (A/O vs. A/V), we performed a 2 × 2 split-plot analysis of variance (ANOVA) of the four mean scores from the Blending Words subtests under each presentation condition, A/V and A/O for each group. Results for all A/O and A/V testing are given in Figure 1, revealing a significant main effects for group, F(1, 56) = 8.34 p = .006, and presentation, F(1, 56) = 21.44, p < .0001, condition, but there was no interaction between group and presentation condition, F(1, 56) = .07, p = .80. In other words, the HC group performed better than the CI group in each presentation condition, but both groups did better in the A/V condition. Additionally, the correlation between scores between the task conditions was .75, with p < .0001 across both groups. For the CI group, the correlation between task conditions was .51, with p < .01, and for the HC group, the correlation was higher at .87, with p < .0001. A z-score difference between these two correlations was computed for the CI and the HC groups and reveals that the correlation between conditions are significantly different by group at −2.71, with p < .01 ( Table 1).

Phonological memory.

Figure 2 presents the mean grade equivalency scores for the phonological memory tasks, Memory for Digits and Nonword Repetition in the both the A/O and A/V conditions. For the Digit Span task, which was not part of the CTOPP, the data are presented in raw scores (right-hand side of panel). For the Memory for Digits task, there was a significant difference in means-CI group: M = 1.94, SE = 0.63, and HC group: M = 4.77, SE = 0.92, t(56) = −2.51, p = .02. For the Digit Span task, there was no significant difference in means-CI group: M = 64.72.07, SE = 4.7, and HC group: M = 65.52, SE = 4.26, t(56) = −.12, p = .90.

Effect of A/V and A/O conditions.

Similar to Blending, it was necessary to examine the effect of the A/V and A/O conditions on Nonword Repetition and Digit Repetition. We performed 2 × 2 split-plot ANOVA on scores from the Nonword Repetition subtests under each presentation condition, A/O and A/V for each group. Results revealed a significant main effects for group, F(1, 56) = 23.06 p < .0001, and presentation, F(1, 55) = 10.45, p < .002, condition, but there was no interaction between group and presentation condition, F(1, 55) = 1.72, p = .20. Again, the correlation between performance on task condition was high across groups at .77, with p < .0001. For the CI group, the correlation between task conditions was .47, with p < .01, and for the HC group, the correlation was higher at .82, p < .0001. Again, a z-score difference between these two correlations was computed for the CI and the HC groups and reveals that the correlation between conditions were significantly different by group at −2.33, p < .01 ( Table 1).

Finally, on the Memory for Digits task, it is interesting to note that the CI group had a lower mean performance on the task than the HC group. However, there was no group difference on the Digit Span task. Recall that the Digit Span task included both auditory and visual presentation of the stimuli, where the Memory for Digits task only included an auditory presentation of the stimuli. Once again in order to examine the effect of the A/V and A/O conditions on Memory for Digits and Digit Span tasks, we again performed 2 × 2 split-plot ANOVA on scores from the Memory for Digits subtests, which can be considered as an A/O condition, and the Digit Span task, which can be considered as an A/V condition. Results revealed no significant main effects for group, F(1, 56) = 0.66 p < .42, but there was an effect for presentation condition, F(1, 56) = 363.63, p < .0001. Additionally, there was no interaction between group and presentation condition, F(1, 56) =0.40, p = .5278. Again, the correlation between performance on task condition was moderate across groups at .42, with p < .01. For the CI group, the correlation between task conditions was .37, with p = .02, and for the HC group, the correlation was at .45, with p = .05. This time, however, when a z-score difference between these two correlations was computed for the CI and the HC groups, it revealed that the correlation between conditions for were not significantly different by group at −.35, with p = .36 ( Table 1). Thus, for the CI group, when digit repetition is done in an A/O condition, it is a more difficult task than it is for the HC group. Yet, the performance of the CI kids in the A/O condition is moderately correlated to their performance on the A/V task.

Rapid naming.

Figure 3 presents the mean grade equivalency scores for the Rapid Naming tasks, including Rapid Number Naming and Rapid Letter Naming. Mean grade equivalency score on Rapid Number Naming for the CI group was 4.2 ( SE = 0.52), whereas the mean grade equivalency score for the HC group was 4.34 ( SE = 0.57). A t test demonstrated that these means were not significantly different, t(56) = −21, p < .84. For Rapid Letter Naming, mean grade equivalency score for the CI group was 5.6 ( SE = 0.61), and the mean grade equivalency score for the HC group was 4.5 ( SE = 0.51). A t test demonstrated that these means were not significantly different, t(56) = 1.3, p = .20.

Pearson correlation was performed on number and letter naming for each group. Results indicated that the correlation between number and letter naming for the HC children was high, r = .87, p < .0001; yet, for the CI children, the correlation was negative and not significant, r = −.25, p > .05.

Nonverbal reasoning tasks.

Figure 4 reveals that the group mean standard score on the UNIT for the CI children was 103.7 ( SE = 2.42), and the group mean standard score for the HC children was 110.1 ( SE = 1.97). A t test demonstrated that these means were significantly different, t(56) = −2.30, p = .03. Although the means are significantly different, the mean standard score for each group was within the average range.

Reading achievement.

Figures 5 and 6 present the results from the Word Attack and Word Comprehension subtest from the WRMT ( Woodcock, 1987), using both the grade equivalency scores and the standard scores. Examination of the mean standard scores between the groups reveals significant differences for Word Attack in the CI group ( M = 101.31, SE = 4.91) and the HC group ( M = 116.93, SE = 2.22), t(39) = −2.90, p = .006. Similarly, the mean standard score for Word Comprehension in the CI group was 93.38 ( SE = 3.37), and the mean standard score for the HC group was 107.93 ( SE = 1.65). A t test revealed that the standard scores between the two groups were significantly different, t(40.7) = −3.88, p = .0004 (Satterthwaite correction for heterogeneity of variance used). The grade equivalency performance of the groups, however, is similar. Recall that the two groups were matched on grade equivalency performance on the Word Comprehension subtest. Although we did not specifically match the groups for performance level on the Word Attack subtest, t-test results revealed no significant difference in mean grade equivalency. The t-test results are t(56) = −.08, p = .86 and t(56) = .06, p = .95, for Word Attack and Word Comprehension, respectively. The groups can be different with respect to one score (standard score), and similar on another score (grade equivalency) is illustrative of how the scores take into account the age of the child when tested, and the relative standing of the score achieved. In this case, the mean ages for the two groups were different. The mean age for the CI children was 11 years 9 months, whereas that for the HC children was 9 years 7 months (a difference of about 2 years 2 months); yet, the groups were performing at similar reading levels.

Distribution of scores.

Figures 7 and 8 present box plots for all the PP tests as a way to illustrate the score distribution for both the CI and the HC groups. In Figure 7, the box plots reveal wide distributions for the tests of Elision and Blending in the A/V condition, Nonword Repetition in the A/V condition, Letter Naming, and Digit Span. In the Figure 8, the distributions are more restricted, especially for the CI group, for the tests of Blending in the A/O condition, Digit Repetition and Nonword Repetition in the A/O condition, Number naming, and the Rhyme task.

Relationship between PP tests, word attack, and word comprehension.

Tables 2 and 3 present the Pearson correlations between the PP subtests and the reading scores for the CI and HC groups, respectively. For the CI group, the PA subtests of Elision and Blending (in the A/V condition) were significantly correlated with Word Attack scores, r = .63, p < .01 and r = .42, p < .05, respectively, and with Word Comprehension scores, r = .70, p < .01 and r = .37, p < .05, respectively. Similarly for the HC group, Elision and Blending (in the A/V condition) were significantly correlated with Word Attack scores, r = .65, p < .01 and r = .40, p < .05, respectively, and with Word Comprehension scores, r = .78, p < .01 and r = .56, p < .01, respectively. Blending in the A/O condition was not significantly correlated for either Word Attack or Word Comprehension in the CI group, and Blending in the A/O condition was significantly correlated only with Word Comprehension for the HC group, r = .52, p < .01. For the CI group, the phonological memory subtests of Digit Span and Nonword Repetition (both presented in the A/V condition) were moderately correlated with Word Attack, r = .37, p < .05 and r = .41, p < .05, respectively, and Word Comprehension, r = .61, p < .01 and r = .38, p < .05, respectively. For the HC group, Digit Span and Nonword Repetition (both presented in the A/V condition) were also correlated with Word Attack, r = .53, p < .01 and r = .55, p < .01, respectively, and even more so with Word Comprehension, r = .74, p < .01 and r = .54, p < .01, respectively. The A/O version of the Memory for Digits task was not significantly correlated for either Word Attack or Word Comprehension, for the CI children. The A/O version of Memory for Digits was not correlated for Word Attack, but it was for Word Comprehension in the HC group, r = .54, p < .01. Finally, for the CI group, Rapid Naming for letters was significantly correlated with both Word Attack and Word Comprehension, r = .49, p < .05 and r = .75, p < .01 and also for the HC group, r = .55, p < .01 and r = .75, p < .01. Rapid Naming for numbers was significantly correlated with Word Attack and Word Comprehension only for the HC group, r = .53, p < .01 and r = .72, p < .01.

Discussion

The first main goal of the study was to investigate whether it was possible to establish a series of tasks to validly measure the PP skills of CI users. Second, we wanted to document the range of the PP skills in children with more than 3 years of CI experience. Finally, we wanted to investigate the relationship between the measured PP skills and reading skills. We briefly review each measure of PP in order to address these goals.

Validity of Tasks

It was previously not clear whether one could validly measure PP skills in deaf children. Validity is defined as the degree to which a task actually measures what it is attempting to measure (Heffner, 2007). An additional consideration with regard to validity is the question of how appropriate are the inferences and decisions we make based upon the results of the child's performance on the tasks. In this case, we were attempting to validly measure PP skills in profoundly deaf children who wear CIs. Assessment of rhyming was possible through a picture identification task, and assessment of sound-based tasks was possible through using standard test materials provided by a commercial test distributor. We found that all the children were able to complete all the tasks administered. That is, they were able to perform the training items, and then, they were able to complete additional test items before they missed enough items to obtain a ceiling performance. This is a prerequisite for achieving a valid assessment. Yet, does the ability to complete tasks designed to measure PP skills in hearing children indicate that the tasks are valid in this population?

We hypothesized that if poorer hearing in the CI children influenced their PP performance, then their performance on parallel tasks that vary in auditory demands will be affected more than what we would find in hearing children with comparable reading abilities. On the Elision subtest (a PA task), we found no significant difference in performance between CI and HC children. Additionally, performance on Elision was correlated with performance on the two reading tasks. On the Blending (also a PA task) and Nonword Repetition task (a task incorporating phonological memory), we found a main effect for group condition (HC children performed better) and for presentation condition (both HC and CI children did better when they were in A/V condition). There was, however, no interaction between group and presentation condition. Again, we found that there was a significant correlation between performance on Blending and the reading tests, plus Nonword Repetition and the reading tests, but only in the A/V condition for the CI group. For the HC group, the correlations remained significant for all conditions, except the A/V condition of Blending and Word Attack. On the Digit Repetition task, although there was no significant main effect for group, there was a main effect for presentation condition and no interaction between group and presentation condition. These findings indicate that to the extent that all these tasks are valid in the HC group, they are also valid in the CI group. Furthermore, we see that performance for the PP tasks are correlated with both Word Attack and Word Comprehension, but that this finding only held for the CI group if they received an A/V condition.

We also hypothesized that the correlations between performances on these parallel tests varying only in mode of presentation (A/O vs. A/V) would differ between the CI and the HC children if hearing affected performance. What we found was that performance on each task presented in the A/O modality was significantly correlated with performance on the task presented in the A/V modality for both groups. We did find that there was a difference in the strength of correlation between groups, however. There was a higher correlation between task performance for the hearing children in the PA tasks of Blending and the PP task of Nonword Repetition. For a Digit Repetition task, however, the strength of correlation between task conditions was similar for the CI and HC groups. This finding provides us further evidence that the tasks are as valid in the CI group as they are in the HC group.

PP: Task Selection, Development, and Theoretical Implications

Certain tasks were better suited for evaluating the PP skills in the children with CIs. In this section, we briefly review all the PA tasks in order to discuss the validity of the tasks and the theoretical implications of the results.

Phonological awareness.

One of the easiest PA tasks for hearing children is the rhyming task ( Adams, 1990). Hearing children usually begin to show rhyme awareness in the preschool years and have mastered the concept by first grade ( Bradley & Bryant, 1991). In the current study, the majority of the CI children could perform the Rhyme task with more than 85% accuracy. Unlike hearing children, however, there were some CI participants in this study who did not achieve ceiling performance even by age 10. The present group of CI children did perform better than the group of younger CI children in the study by James et al. (2005). In that study, 20 CI children achieved a mean accuracy level of 56% and then 77% at mean age levels of 8 years 5 months and 9 years 5 months, respectively. Those children also had, on average, fewer years of CI experience (4 and 5 years, respectively). Taken together, the findings of this study and the study of James et al. indicate that awareness of syllables and rhyme emerges gradually over time for most but not all of the CI children.

For the Blending Words task, the CI children performed nearly uniformly at the early-elementary level (up to third-grade equivalencies) regardless of their chronological ages and of presentation condition (A/O or A/V). In contrast, the HC children tended to achieve ceiling performance by age 10. This finding indicates that the HC children master the blending skill by age 10, but that the CI children are still developing this skill throughout their elementary years and even into their teen years. In the continuum of difficulty of PA skills, blending is considered to be an intermediate skill ( Adams, 1990; Stahl & Murray, 1994).

The CI children fared much better on the Elision task, one of the harder PA skills ( Adams, 1990; Stahl & Murray, 1994). Both the HC and the CI children tended to display a well-distributed performance pattern across age ranges. Even so, there was not a pattern of uniform mastery on Elision by a specific age for the CI children. Where the HC children demonstrated evidence of mastery on the task by age 10, there were approximately six children in the CI group who performed below the fourth-grade level after age 10.

The implications of these findings reveal that although there is certainly evidence that children with CIs do develop PA skills, their performance is characterized by a longer, more protracted learning phase before mastery is achieved than their HC counterparts. Interestingly, the CI children performed fairly well as a group on the harder task of Elision. One explanation for this result could be attributed to the nature of the task, which requires the child to first say the word (e.g., "bold"). Then, the child is to repeat the word without saying a part of the word (e.g., "b") as in "Say 'bold' without the 'b'." The first task of saying the word may serve two purposes. In the case of the children with a CI, the examiner can verify that the child hears the correct word. Also, the child produces the movements for the whole word. This act of articulating the whole word could facilitate awareness of the "mental model" of the parts of the word (Leybaert & Alegria, 1995). The Elision task, when completed in this manner, is thus felt to be a particularly useful task to assess PA, in children with hearing loss or with CIs because it assures that the child has the correct word in mind.

In summary, the CI children performed best on rhyming, with a near ceiling effect. In contrast, they had a near floor effect for the Blending task, but the performance on the Elision task was well distributed. The performance differences within the CI group across the tasks could indicate that rhyming is a precursor skill to reading and that Elision tasks are well suited for assessing PA skills in children with CIs who are at this age and developmental level.

Phonological memory.

On the phonological memory subtests, we frequently noted differences between the CI and the HC groups with regard to performance on most tasks. There were significant differences in group performance on the Nonword Repetition task for both the A/O and the A/V modalities. On the Nonword Repetition task, the CI children had a nearly uniform performance at the early-elementary level (kindergarten to second-grade equivalencies) regardless of their age in the A/O presentation condition, with slightly better performance (up to fourth grade) in the A/V condition. In contrast, the HC children tended to achieve ceiling performance by age 10 regardless of presentation condition (i.e., A/O or A/V). On the Digit Repetition task, there were significant differences in performance between groups with higher mean scores for the HC group. The results are in concert with the findings of others in that children with CIs tend to display shorter working memory for verbal and spatial patterns than their hearing counterparts ( Burkholder & Pisoni, 2003; Cleary, Pisoni, & Geers, 2001).

For the Digit Span task, the two groups performed similarly and the distribution of scores was uniform for both groups. The Digit Span task used visual presentations of digits on a computer monitor, paired with auditory presentations. Additionally, the response modality was the computer keyboard. The distributed results and the similarities seen between groups on the Digit Span task, tested using the described methodology in this study, are in contrast with the above-mentioned studies that found significant short-term memory differences between children with CIs and those with hearing. This finding suggests that the visual/auditory/ keypad testing method for digit Span for CI children is more indicative of their true short-term memory skills, and these results reveal that there may not be memory differences between the CI and the HC groups. This response method appears to be less susceptible to the effects of the distorted auditory signal. In addition, the use of the keypad for responding eschews the effects of slowed or distorted speech production. These results also support the finding by Burkholder, Pisoni, and Svirsky (2005) that for adults with hearing, digit span was reduced if they were given a degraded auditory signal (an eight-channel, frequency-shifted acoustic simulation of a CI). The authors attributed the performance decline to misidentification or to an incorrect encoding of digits due to the degraded signal rather than to ineffective subvocalization rehearsal or serial scanning of phonological representations in the short-term memory task. Given this logic, the same precautions should be made in testing the memory skills in prelingually deaf children who use CIs, who would be even more likely to have performance declines related to listening and speaking. Unlike hearing adults who have well-developed audition, children with CIs have never had hearing, nor do they have the articulation skills of adults. Thus, testing memory using vision, sound, and keyboarding could be a more accurate method of assessment for the CI children; yet, this now adds a confound of including visual, short-term memory. At any rate, the method of testing in this study is a compromise to best avoid the confound of perceptual skills and underscores that we need to use caution in testing memory in this population.

Rapid naming.

Naming speed has been found to be predictive of early reading skill because it is thought to be a measure of the efficiency of retrieving phonological codes associated with phonemes, parts of words, or entire words ( Shankweiler & Crain, 1986; Share, 1995; Torgesen & Burgess, 1998). For Number Naming, the CI group demonstrated a lot of variability in their performances, with a very unequal distribution of scores. The CI group never did evidence mastery of the skill (i.e., never displayed ceiling performance), even after age 12. In contrast, both groups demonstrated variable and similarly distributed performances on Letter Naming.

According to the authors of the CTOPP, performance with number and letter naming is highly correlated in children with hearing ( Wagner et al., 1999). In this study, we replicated this finding with the HC group but not for the CI group. The correlation between number and letter naming for the HC children was high, r = .87, p < .0001; yet, for the CI children, the correlation between number and letter naming was negative and not significant, r = −.25, p > .05. The discrepancy between the Letter Naming and Number Naming results in the CI group tends to support the idea that they were better at retrieving the sounds or name codes associated with the letters than for numbers. This indicates that for the CI children, the act of vocalizing the names of numbers takes more time than the act of vocalizing the names of letters. Again, it is difficult to tell for certain whether letter-naming proficiency is a precursor to reading or result of reading; yet in this study, for some reason letter naming was associated with reading in a way that is more closely related than is the act of naming numbers. The CI children in this study were familiar with sign language. As a result, it is likely that their school experience as preschoolers included listening to the names of letters as they were simultaneously signed and spoken during finger-spelling and letter-learning activities. This could mean that letter names are highly salient for the children, and the names of numbers are less salient. The task of letter naming is therefore evidence of very well-learned phonological representations, where the task of number naming is not.

Thus, given the above-mentioned findings, a possible PP test battery for children who have several years of CI experience and who are beginning to read could include a rhyme task, an elision task, an A/V digit span task as described above, and a letter-naming task.

The results of this study indicate that although children with CIs can complete tasks in an A/O condition, the A/V testing condition was significantly correlated with Word Attack and Word Comprehension. As such, the A/V condition may be a more accurate measure of the actual PP skills and is not as susceptible to artifact from the degraded sound signal, yet either way, the tasks yield a valid measurement of skill. A challenge with the current version of the blending-type and nonword repetition-type tasks is that young children, particularly those with CIs, find the tasks quite difficult and are therefore susceptible to floor effects.

Relationship Between PP test and the Reading Skills of Word Attack and Word Comprehension

This study illuminates the importance of assessing the PP skills in children with CIs. PP skills correlated with the reading skills of decoding words and understanding words in children with CIs. Similarly, PP skills are correlated with reading skills in hearing children. The presence of this correlation in this study provides evidence that the tasks used within the study were indeed tapping skills that relate to reading achievement. With assessment of PP skills, we can potentially identify those CI users who are significantly below their peers with respect to their PP abilities and consequently which children need extra intervention to increase their PP skills. The results of this study indicate that clinicians can use PP assessment information to identify a particular child's areas of strength and weakness regarding phonological skill development. This information can hopefully be used in a proactive manner to support the development of strong reading skills.

Future Directions and Considerations

We would expect there to be a positive relationship between PP, word reading, and reading comprehension in children with CIs, based on the literature that finds this relationship in children with hearing. This study indicates that it is possible to find tasks that can assess the PP skills of children who are deaf and who use CIs. Furthermore, we did find a correlation between PP skills and reading skills. A subsequent study in progress will conduct a more fine-grained analysis of the relationship between the phonological skills and the reading skills in the children with CIs. That study will provide an idea of how much of the variability in reading skills of CI users can be predicted by their phonological skills.

The Reciprocal Nature of PA

Some argue that PA comes about as a result of learning to read, or what is known as the notion of reciprocal causation. We see evidence of reciprocal causation when we examine the PA skills of illiterate adults. Morais, Bertelson, Cary, and Alegria (1986) compared PA skills in adults who have never received reading training and with adults who received reading training in adulthood. The former group had significantly inferior phonemic segmentation skills than the latter group, suggesting that PA skills do not develop naturally but are learned. Furthermore, if PA skills improve after literacy training, this may indicate that one develops the ability to reflect on spoken words after learning to read.

In the context of children with CIs, it could be that this reciprocal relationship is more evident than in the context of children who hear. For example, there could be instances where a deaf child who uses a CI does not hear all the sounds of a word but comes to realize that there are sounds that are included within the word only after seeing the word in print. Alternatively or additionally, some children may come to the reading task with a more developed sense of what words sound like.

In this particular set of participants, the average age at implantation was just over 3 years 6 months; the average length of CI experience was 8 years. In spite of the relatively "late" age of implantation by today's standards, these children could produce nearly 88% of the phonemes correctly on a sentence repetition task. One could predict that with early identification of profound hearing loss, and earlier implantation, the listening and speaking skills of the CI children will continue to be well developed in the future. The results of this study set the stage to begin formulating prospective, controlled studies designed to explore the contribution of age and duration of implant use on the subsequent skills such as PP, which are thought to contribute to literacy as suggested by Marschark, Rhoten, and Fabich (2007).

Similar Reading Levels and Similar PP Levels?

Both groups of children in this study were decoding words at the early ninth-grade level and comprehending words at the early fifth-grade level. There was a strong correlation between PP skills and reading skills for both groups. At first blush, it appears that the HC group had much higher grade-level performance on several of the PP tests (e.g., Blending, Nonword Repetition); yet, performance for Rhyme and Elision and Digit Span was similar for both groups. This outcome suggests that there are subtle ways that children with CIs may go about the process of reading differently from their hearing children. Further studies could explore the influence of the "resilient reader" effect posited by Jackson and Dollinger (2002) who speculated that resilient readers might have more skills with using contextual cues for comprehension.

Funding

This research was supported in part by research Grant 5 P50 DC00242 from the National Institutes on Deafness and Other Communication Disorders, National Institutes of Health; Grant M01-RR-59, National Center for Research Resources, General Clinical Research Centers Program, National Institutes of Health; the Lions Clubs International Foundation; and the Iowa Lions Foundation.

No conflicts of interest were reported.

References

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Evaluating Phonological Processing Skills in Children With Prelingual Deafness Who Use Cochlear Implants | The Journal of Deaf Studies and Deaf Education | Oxford Academic

# data[].attributes.attachment_link.description
For many years, people who are stakeholders in the task of fostering the communication skills of children with severe-to-profound hearing loss have described the relationship between intervention, language, and literacy outcomes. This descriptive research describes generally low achievement levels across communication skills including speech intelligibility, spoken English, and written English comprehension.