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Introduction: Managing Celiac Disease in Learning Environments

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UFLI How Children Learn to Read Words - Ehri’s Phases

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Language

Language is a rule-governed behavior. It is defined as the comprehension and/or use of a spoken (i.e., listening and speaking), written (i.e., reading and writing), and/or other communication symbol system (e.g., American Sign Language).

Spoken and written language are composed of receptive (i.e., listening and reading) and expressive (i.e., speaking and writing) components.

Spoken language, written language, and their associated components (i.e., receptive and expressive) are each a synergistic system comprised of individual language domains (i.e., phonology, morphology, syntax, semantics, pragmatics) that form a dynamic integrative whole (Berko Gleason, 2005).

Descriptions of the five language domains follow:

Phonology-study of the speech sound (i.e., phoneme) system of a language, including the rules for combining and using phonemes.

Morphology-study of the rules that govern how morphemes, the minimal meaningful units of language, are used in a language.

Syntax-the rules that pertain to the ways in which words can be combined to form sentences in a language.

Semantics-the meaning of words and combinations of words in a language.

Pragmatics-the rules associated with the use of language in conversation and broader social situations.

The five basic language domains are part of a continuum which spans to higher order language skills, such as discourse, which is impacted by skills in the pragmatics domain.

Higher order language skills include inferencing; comprehension monitoring; interpretation of complex language, such as jokes and puns; and use of text structure knowledge. Metalinguistic awareness is requisite for the development of higher order language skills and is defined as "the ability to think about and reflect upon language" (Gillon, 2004, p. 10). Metalinguistic awareness includes phonological awareness, morphological awareness, syntactic awareness, semantic awareness, and pragmatic awareness. Metalinguistic skills are also critical for self-regulation and self-monitoring.

Spoken Language

Written Language

Pragmatics

(includes discourse)

understanding of the social aspects of spoken language, including conversational exchanges

social use of spoken language, including production of cohesive and relevant messages during conversations

understanding point-of-view, needs of the audience, etc.

conveying point-of-view, intended message, etc.

See ASHA's resource titled Developmental Norms for Speech and Language for more information.

Language Acquisition and Use

Language learning and use are determined by the interaction of biological, cognitive, psychosocial, and environmental factors. Language evolves within specific historical, social, and cultural contexts. Communication difference/dialect is a variation of a linguistic symbol system used by a group of individuals that reflects and is determined by shared regional, social, or cultural/ethnic factors (ASHA, 1993).

Effective use of language for communication requires an understanding of human interaction, including nonverbal cues, motivation, and sociocultural roles.

Relationship Between Spoken Language and Written Language

Phonological awareness underlies the ability to manipulate speech sounds (i.e., phonemes) in spoken words. It has been found to contribute notably to reading and writing development (Al Otaiba, Puranik, Zilkowski, & Curran, 2009; Lemons & Fuchs, 2010; Scarborough, 1998). Components of phonological awareness include syllable awareness (e.g., one syllable in "c ap" vs. two syllables in " ag ain"), onset-rime awareness (e.g., onset: cap vs. rime: c ap), and phoneme awareness (e.g., "cap" contains three phonemes: /k/ + /æ/ + /p/)

When instruction in phonological awareness is paired with knowledge of letter names (i.e., graphemic awareness), then phonics, a core written language skill for reading and writing development, is being addressed.

See ASHA's Practice Portal pages on Spoken Language Disorders and Written Language Disorders for more information.

Language Disorder

A language disorder is an impairment in comprehension and/or use of a spoken, written, and/or other communication symbol system (e.g., American Sign Language). The disorder may involve the form of language (phonology, morphology, syntax), the content of language (semantics), and/or the function of language in communication (pragmatics) in any combination (ASHA, 1993). In some cases, augmentative/alternative communication may be required for individuals demonstrating impairments in gestural, spoken, and/or written modalities. (ASHA, 1993).

Language disorders may persist across the lifespan, and symptoms may change over time (Bashir, 1989). Further, a language disorder can be a distinct diagnosis or may occur within the context of other conditions.

A regional, social, or cultural/ethnic variation of a symbol system should not be considered a disorder of speech or language (ASHA, 1993).

Relationship Between a Language Disorder and Social Communication Disorder

Children with language disorders may also present with social communication difficulty since social communication comprises social interaction, social cognition, pragmatics, and language processing. More information about social communication can be found on ASHA's Social Communication Disorder Practice Portal site.

References

Al Otaiba, S., Puranik, C., Zilkowski, R., & Curran, T. (2009). Effectiveness of early phonological awareness interventions for students with speech or language impairments. Journal of Special Education, 43(2), 107-128.

American Speech-Language-Hearing Association. (1993). Definitions of communication disorders and variations [Relevant Paper]. www.asha.org/policy/.

Bashir, A. S. (1989). Language intervention and the curriculum. Seminars in Speech and Language, 10(3), 181-191.

Berko Gleason, J. (2005). The development of language (6th ed.). Pearson Education.

Gillon, G. T. (2004). Phonological awareness: From research to practice. Guilford Press.

Lemons, C.J., & Fuchs, D. (2010). Phonological awareness of children with Down syndrome: Its role in learning to read and the effectiveness of related interventions. Research in Developmental Disabilities, 31(2), 316-330.

Scarborough, H. S. (1998.). Early identification of children at risk for reading disabilities: Phonological awareness and some other promising predictors. In B. K. Shapiro, P. J. Accardo, & A. J. Capute (Eds.). Specific reading disability: A view of the spectrum (pp. 75-119). York Press.

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Language is a rule-governed behavior. It is defined as the comprehension and/or use of a spoken (i.e., listening and speaking), written (i.e., reading and writing), and/or other communication symbol system (e.g., American Sign Language).

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The Texas Education Agency (TEA) defines accommodations as changes to materials or procedures that enable students to participate meaningfully in learning and testing. Accommodation policies may apply to any student taking the State of Texas Assessments of Academic Readiness (STAAR ®) or the Texas English Language Proficiency Assessment System (TELPAS) depending on his or her needs and whether the student meets the eligibility criteria, as applicable. These policies are divided into three main categories:

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Accommodations - District and Campus Coordinator Resources - Confluence

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The Texas Education Agency (TEA) defines accommodations as changes to materials or procedures that enable students to participate meaningfully in learning and testing. Accommodation policies may apply to any student taking the State of Texas Assessments of Academic Readiness (STAAR ®) or the Texas English Language Proficiency Assessment System (TELPAS) depending on his or her needs and whether the student meets the eligibility criteria, as applicable.

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Dictionaries must be available to all students taking the following STAAR assessments: grades 3-8 RLA, Spanish grades 3-5 RLA, English I, and English II. A dictionary tool appropriate for these tests and that fulfills this requirement is available for student use in the online testing platform.

For students testing online with access to an additional dictionary or students testing on paper, the following information applies.

Districts may provide dictionaries, or students may bring them from home.

To the extent allowable, students should be provided or allowed to use the same type of dictionary during testing that they routinely use in the classroom. Providing an unfamiliar dictionary on the day of the state assessment may hinder rather than aid the student.

The following types of dictionaries are allowable:

standard monolingual dictionaries in English or the language most appropriate for the student

dictionary/thesaurus combinations

bilingual dictionaries*

English as a second language (ESL) dictionaries* (defines English words using simplified English)

sign language dictionaries

picture dictionaries

electronic dictionaries

One dictionary for each student taking these assessments is recommended. There must be a minimum of one dictionary for every five students testing.

Thesauruses are allowed, either in combination with a dictionary or as a separate resource, but they are not required. If provided, there should be one thesaurus for every five students taking these assessments.

Additional sections of a dictionary (e.g., abbreviations, biographical or geographical entries, style or grammar guides) do not have to be restricted, as they are included in the dictionaries routinely used in the classroom.

For electronic dictionaries, including apps, all internet capabilities must be disabled for use during testing. In addition, the dictionary app being used must be locked down or in kiosk mode to prevent the use of other apps during testing. Refer to the Technology Guidelines page for more information regarding the security and validity of the assessments.

Specialty dictionaries such as teacher-made, student-made, subject-specific, or slang dictionaries are not allowed.

* A bilingual or ESL dictionary should be provided based on the individual student's needs and how much the student uses it during classroom instruction and testing. It is important for LPACs to consider the degree to which an emergent bilingual student relies on a dictionary during instruction and testing when making reclassification decisions.

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STAAR Dictionary Policy - District and Campus Coordinator Resources - Confluence

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Dictionaries must be available to all students taking the following STAAR assessments: grades 3-8 RLA, Spanish grades 3-5 RLA, English I, and English II. A dictionary tool appropriate for these tests and that fulfills this requirement is available for student use in the online testing platform.

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Welcome to FCRR! We are dedicated to ensuring that all children are reading and succeeding in school. Leadership is critical to achieving that vision. Here, school and district leaders can find resources to support their knowledge and practice. FCRR invites you to use the resources below to make evidence-based and evidence-informed decisions to improve reading outcomes for all learners.

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Welcome to FCRR! We are dedicated to ensuring that all children are reading and succeeding in school. Leadership is critical to achieving that vision. Here, school and district leaders can find resources to support their knowledge and practice. FCRR invites you to use the resources below to make evidence-based and evidence-informed decisions to improve reading outcomes for all learners.

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Literacy for All is a Course

Literacy for All

Self-paced
6 credits

Enroll

Full course description

Literacy for All! Designing Inclusive Literacy Learning Environments is designed to help educators understand the importance of teaching students who struggle to read on grade level in grades 4-12 foundational reading skills. You will learn about the science of teaching reading, including an overview of what skills need to be mastered to become a proficient reader, how the brain learns to read, the characteristics of dyslexia and dysgraphia, how a student's learning abilities associated with reading can impact a student's ability to learn how to read, and what instruction and accommodations can remove barriers to learning. This session was developed by the Inclusion in Texas Network in collaboration with the Texas Education Agency (TEA).

Sign up for this course today!

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Literacy for All - Inclusion in Texas Network

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Literacy for All is a Course Self-paced 6 credits Enroll Literacy for All! Designing Inclusive Literacy Learning Environments is designed to help educators understand the importance of teaching students who struggle to read on grade level in grades 4-12 foundational reading skills.

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Texas Educator Certification Examination Program

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Texas Educator Certification Examination Program

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Field 293 - Science of Teaching Reading Examination Framework - February 2020

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UFLI - How Children Learn to Read Words: Ehri's Phases

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UFLI - How Children Learn to Read Words: Ehri's Phases

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UFLI How Children Learn to Read Words - Ehri’s Phases

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stages young children go through in developing their spelling skills 

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stages young children go through in developing their spelling skills 

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Learn about the different stages young children go through in developing their spelling skills and how phonetic or "invented" spelling can play an important role in helping children learn how to write.

Children advance through identifiable stages in their development as spellers. These stages have been described by several different researchers but all derived from the research of Charles Read and Edmund Henderson in 1971.

Researchers began to understand that learning to spell is not a matter of merely memorizing letter sequences, but of developing and applying linguistic knowledge as well as knowledge of letter-sound relationships and vowel patterns.

Children's spellings provide a window into their understanding of language, which is informative for understanding their reading as well as their spelling.

High-quality spelling instruction includes building upon a child's word knowledge and enabling them to move from one stage to the next.

Learn the stages of spelling development below.

Precommunicative stage

The child uses letters from the alphabet but shows no knowledge of letter-sound correspondences. The child may also lack knowledge of the entire alphabet, the distinction between upper- and lower-case letters, and the left-to-right direction of English orthography (e.g., the letter M used for the word Jessica).

Semiphonetic stage

The child begins to understand letter-sound correspondence - that sounds are assigned to letters. At this stage, the child often employs rudimentary logic, using single letters, for example, to represent words, sounds, and syllables (e.g., U for you).

Phonetic stage

Children use a letter or group of letters to represent every speech sound that they hear in a word. Although some of their choices do not conform to conventional English spelling, their attempts to spell words are systematic and easily understood (e.g., the letters tak for take and en for in).

Transitional stage

The speller begins to assimilate the conventional alternative for representing sounds, moving from a dependence only or mainly on phonology (sound) for representing words, to evidencing a greater understanding of common letter patterns in words and the structure of words. Some examples of misspelling typical of this stage are egul for eagle and higheked for hiked.

Correct stage

At this stage, spellers know common letter-sound relationships and generalizations (rules) for spelling, as well as how to use morphemic information in spelling. The student understands how to spell many common prefixes and suffixes, silent consonants, alternative spellings, and irregular spellings. A large number of learned words are accumulated, and the speller recognizes incorrect forms. The child's generalizations about spelling and knowledge of exceptions are usually correct.

The progression through each stage is gradual. In samples of student writing, you will often see examples from more than one stage. Also, the fact that children progress through identifiable stages in their spelling does not mean that spelling development is spontaneous and will simply unfold "naturally," without instruction. To the contrary, direct instruction in spelling is vital and shapes children's progression through each of the stages.

"Invented" spelling

Invented spelling refers to a child's use of phonetic spelling strategies in attempting to write unknown words. For example, a young child might spell the word cake as kak or the word garage as guraj.

The use of invented spelling has some important advantages for beginning spellers. First, it allows young children to express their thoughts in writing without become excessively focused on spelling every single word correctly. Also, there is evidence that when children are encouraged to use invented spelling properly - for example, to say an unknown word orally and "stretch" it, listening for and transcribing each sound - the use of invented spelling may help promote important reading-related abilities such as phonemic awareness and phonics skills.

Does "invented" spelling interfere with standard spelling?

If used properly at the beginning stages of spelling development, phonetic or "invented" spelling can play an important role in helping children learn how to write. When children use phonetic spelling, they are applying their growing knowledge of phonemes and letters, and developing their confidence in the alphabetic principle. A child's iz for the conventional is can be celebrated as quite a breakthrough! This kind of error shows us that the child is thinking independently and quite analytically about the sounds of words and the logic of spelling.

However, explicit teaching of conventional spelling from the earliest grades is also very important. Without explicit instruction, most children will not simply infer the correct spelling of all the words they need to learn in order to write well. A thoughtful approach should be used, combining an understanding of invented spelling with formal spelling instruction.

Invented spelling

In a Connecticut suburb, first grade teacher Carol Spinello turns a spelling lesson into something of a game.

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Learn about the different stages young children go through in developing their spelling skills and how phonetic or "invented" spelling can play an important role in helping children learn how to write. Children advance through identifiable stages in their development as spellers.

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Systematic Instruction: Scope & Sequence  (tealearn.com)

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Systematic Instruction: Scope & Sequence  (tealearn.com)

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Basic English Phonics Scope & Sequence

Basic English phonics scope & sequence includes:

Consonants and short vowel sounds

Consonant diagraphs

Consonant blends

Vowel-Consonant Silent e

Vowel digraphs

Vowel controlled r

Diphthongs

Consonant -le

Common syllable patterns

Common suffixes

Common prefixes

Suffix rules in spelling

Basic Grammar Scope & Sequence

Basic grammar scope and sequence includes:

NOUNS

Common

Proper

Singular & plural

Irregular plural

Possessive nouns

VERBS

Action

Linking

Helping

Irregular

Tense

Agreement

Basic Composition Scope & Sequence

Basic composition scope and sequence includes:

SENTENCE

Subject

Predicate

Simple Sentence

Compound

Complex

Conjunctions

Clauses

Independent

Dependent

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Modes of Reading

Types of reading and scenarios where these types of reading may be useful in the classroom

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Modes of Reading | Bill of Rights Institute

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A Teacher Support resource to support student literacy.

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Key Language Uses in writing in both Spanish and English

Usando los usos clave para la escritura bilingüe

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The Key Language Uses in Bilingual Writing

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This WIDA Webinar examines the Key Language Uses in writing in both Spanish and English. A WIDA expert explores an analysis of bilingual writing examples and mentor texts using the lens of the Key Language Uses and los usos clave del lenguaje del Marco DALE.

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WIDA Standards

WIDA English Language Development (ELD) Standards Framework, 2020 Edition, provides a continuum of language development for multilingual learners, focusing on how language is used to engage with academic content.

https://wida.wisc.edu/resources?keys=materials&field_category%5B10%5D=10&field_type_target_id%5B18%5D=18

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Biliteracy from the Start,

Dr. Kathy Escamilla

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Kathy Escamilla - Biliteracy from the Start

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EAK Instruction Strategies for Early Childhood

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Using Writing in The (Reading) Classroom–The Amazing Success of First Year Teacher Emily Fleming, Teach Like a Champion

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[caption id=“attachment_17013” align=“aligncenter” width=“617”] Great task; great directions; great results[/caption] Last week I had the…

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Phonological Awareness with Dr. Matt Burns -TRL MD’s 2024 Series: National Reading Panel Report

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Named for their creator, psychologist D.B. Elkonin, Elkonin boxes are a teaching tool that helps strugging readers learn to break words down into their component sounds. This free online Elkonin box tool, allows the student to drag a colored circle into a box while listening for each sound or phoneme. Students and teachers can customize the activity by changing the color of the circles or increasing the number of boxes to be used, as their literacy skills increase. Help students develop literacy skills in decoding and spelling with this simple online educational activity.

Check out more information on early English literacy at ClassPlayground.com, including free printables with blank Elkonin Boxes.

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Elkonin Boxes | Free Educational Activities | Toy Theater

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Named for their creator, psychologist D.B. Elkonin, Elkonin boxes are a teaching tool that helps strugging readers learn to break words down into their component sounds. This free online Elkonin box tool, allows the student to drag a colored circle into a box while listening for each sound or phoneme.

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What are Elkonin Boxes?

Elkonin Boxes are a visual tool used to help children break down words into individual sounds or phonemes. They are typically drawn as a series of squares, with each box representing one sound in a word. For example, the word 'cat' would be divided into three boxes, representing the /c/, /a/, and /t/ sounds respectively.

The Role of Elkonin Boxes in Phonological Awareness

Phonological awareness refers to the understanding that spoken language is made up of individual and separate sounds. It's a crucial step in learning to read and write, as it allows children to understand the relationship between sounds and letters.

Elkonin Boxes play a significant role in developing this awareness. They provide a visual representation of the sounds in a word, helping children to see the segmentation of the word. This helps them understand that even though a word is a single unit when we say it, it's made up of individual sounds.

Using Elkonin Boxes in the Classroom

Using Elkonin Boxes is a straightforward process. The educator says a word slowly and clearly, and the child pushes a token into a box for each sound they hear. For instance, for the word 'dog', the teacher would say /d/ - /o/ - /g/, and the child would push a token into three separate boxes.

Over time, as the child's phonological awareness improves, they can start to use Elkonin Boxes independently. They can also begin to use them for more complex words with blends or digraphs.

The Benefits of Elkonin Boxes

Elkonin Boxes offer multiple benefits for early literacy development:

Building Phonological Awareness: They help children understand the concept of individual sounds in words, which is crucial for reading and spelling.

Enhancing Listening Skills: Children need to listen carefully to distinguish the different sounds in words, which can improve their overall listening skills.

Encouraging Active Learning: Moving tokens into boxes makes learning interactive and engaging, which can help children stay focused and retain information better.

Providing Visual Support: For visual learners, seeing a word broken down into individual sounds can make the concept more concrete and understandable.

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What are Elkonin Boxes? Elkonin Boxes are a visual tool used to help children break down words into individual sounds or phonemes. They are typically drawn as a series of squares, with each box representing one sound in a word. For example, the word 'cat' would be divided into three boxes, representing the /c/, /a/, [...]

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Accessing Early STAAR Results for District and Campus Administrators (Spring 2024)

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Spanish Mini Alphabet Mouth Picture Articulation Phoneme Sound Cards Posters | Mar Y Sol Bilingual

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These Mini Spanish mouth articulation alphabet cards are perfect for your Spanish sound wall. They are science of reading aligned and support your dual language literacy instruction by providing student friendly and clear images with with proper mouth positions and articulation to produce sounds. This works well in a dual-language classroom but can also work for Spanish teachers or native language literacy classes. Highlights-	These are mini STUDENT sized (appx 4X5)	5 vocales 24 consonante cards	5 Digraphs Includes both /g/ fuerte y suave	Includes both /c/ fuerte y suave	Varied skin tones	Use to build your classroom sound wall	Print 1 per page and display in the classroom	Print 2 or 4 per page and use small group for students	Use as flashcards	Use as an alternative to the teacher lowering their mask when modeling sounds	Supports the development of language Domains (You can find my 4+1 Language Domains here)	Aligned to the science of reading

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Resources for Dual Language Schools, Parents, and Teachers

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There are three pillars of Dual Language Education (DLE): academic achievement in both languages, biliteracy and bilingualism, and sociocultural competence. Usually, the focus of Dual Language Instruction (DLI) rests upon bilingualism and maybe biliteracy. Academic achievement in both languages and sociocultural competence usually take second and third seats respectively. Let us focus on high academic achievement in both languages for all students today.

While there are many reasons why we provide DLE to our students including the preservation of culture and identity, one of our primary reasons to provide DLE is to ensure that we are closing the opportunity gap for our Emergent Bilinguals, who are largely our Black and Brown students. Unfortunately, when our students are not making academic progress, the fastest solution people choose is always to cut our DLE programs. They quickly claim that DLE programming does not work. However, we have research-based evidence that when DLE programs are properly implemented, they do support students' academic growth and are actually the only bilingual instruction model that closes the opportunity gap for English Learners. However, we also know that when DLE programs are not properly implemented, they do not close the opportunity gap.

When students are not making progress in monolingual math classes, do we cut our math classes? Do we say, "Teaching math doesn't work?" Of course not. We revamp Tier 1 instruction and build MTSS structures to support those students who are not making adequate progress in Tier 1. We should do the same in our DLE programs.

It is for this reason that I have created The 7 Sides of Dual Language Instruction ™. I imagine DLI as a heptagon, a shape as uncommon as the concept of bilingualism seems to be in the United States. If any of the sides are left out, the shape is no longer formed, just as if any of the concepts in my framework are left out, our DLI becomes weak. The 7 Sides of Dual Language Instruction™ are the research-based sides of DLI that need to be present in instruction for us to attain high academic achievement for our students.

1. Structures - For DLI to be successful, there have to be clear structures in place. You will see that structures will influence the other sides of the instructional heptagon as well. An example of structure is the language allocation plan. Are you a 90-10 model, or a 50-50 model, or something in between? Has the language allocation plan been thought out based on the community's needs? Have administrators, teachers, students, families, and other stakeholders been involved in these decisions? How are the content area classes and units being divided amongst the languages to adhere to the language allocation plan? Are the teachers following these plans faithfully? Another example of structures are assessments and MTSS structures.

2. Separation of Language & Translanguaging & Cross-Linguistic Spaces - Recently, this has been a place of controversy where the argument has centered around separation of language translanguaging. (The concept of cross-linguistic connections often falls by the wayside.) However, the key word is not but . While there needs to be separation of language on the part of adults in the classroom, students, especially younger students, should be able to use their entire linguistic repertoire. In other words, students need to be able to use everything they know in every language they know in order to grapple with the material. This of course should never take away from professional decisions made by teachers. For instance, in order to teach specific language objectives, the teacher may choose to have students stay in the language of instruction for a particular activity, and therefore, provide the scaffolds and supports necessary for students to stay in the language. Older students who can stay in the language may be asked to do so as well. Or there may be intentional translanguaging by the teacher such as referring to a family member by their Spanish title during English time or referring to a book they read in the other language that elevates the culture. Furthermore, during the space supporting cross-linguistic connections, also known as bridging, both the adults and students may be translanguaging, but this should always be an intentional, planned space.

3. Speaking Practice and Schema - Academic discourse is important for all students, but especially for language learners. While educators may provide word banks, sentence stems, and other structures in accordance with students' language proficiency, students need to develop their oracy skills. Students need the opportunity to speak both before starting literacy units but also throughout students' literacy instruction. Activities that can provide students with opportunities to speak include but are not limited to Readers' Theatre, Total Physical Response, Whole Class Discussions, Structured Group Work, Debates, Turn and Talks, and Fish Bowl. Activities that can help build schema include the Language Experience Approach, class discussions, and even field trips (virtual ones work too!).

4. Standards - All grade-level content, language, and social justice standards should be addressed. Just because students are in DLE classes should not mean that we are watering down the curriculum. We are still teaching grade-level content in all subject areas. The caveat, however, is that we do not repeat standards across languages. What is taught in Spanish or French or Portuguese or Mandarin is not then retaught in English or vice-versa. Instead, we help students transfer their knowledge to the partner language. Furthermore, every DLE teacher needs to see themselves as both a content and a language teacher. It is important that we are thinking about the language that will move our students to the next level and that we integrate both content and language together. Finally, let us not forget the social justice standards that should be interwoven throughout the lessons in order to reach the third pillar of DLE - sociocultural competence.

5. Struggle - Often when we talk about grade-level standards, we hear that it is not fair for DLE students to have to do grade-level work when they are learning two languages. It is important to remember that bilingualism and multilingualism is the norm in many countries, especially in countries with strong education systems. Students have to be given grade-level standards (or higher) and be expected to productively struggle with them. The amount of struggle expected should be a Goldilocks-level of struggle. They should not struggle too little, or they will either be bored or surface-level engaged without growth. The struggle should also not lead them to frustration, or they will give up. Instead, there should be a productive struggle that leads students to growth. This Goldilocks-level of struggle will be different for each child and therefore, lessons need to be differentiated for each student.

6. Scaffolds and Supports - To teach students grade-level standards while ensuring a Goldilocks-level of struggle, we need to provide students with appropriate scaffolds and supports. These scaffolds and supports may include gestures, vocabulary introduction before the lesson, graphic organizers, sentence stems, and/or word banks. Because the Goldilocks-level of struggle will be different for each child, scaffolds and supports should look different as well. It is also important to build schema for our students before embarking upon a unit. Students can successfully work at higher levels when they know about the topic they are reading.

7. Subject-related and language-based literacy - The more we teach multilingual learners language in context, the faster they will grow. Therefore, it is important to teach integrated, biliteracy units. Social Studies and Science need to be integrated into literacy units, which means we need to look at scheduling and time on learning differently than in monolingual classrooms. Furthermore, partner languages should not be taught with translated pedagogy. The way you learn phonics-based languages is different from how you learn English. These differences should be honored.

If The 7 Sides of Dual Language Instruction ™ are properly implemented in Tier 1 instruction, I am confident that programs will attain the first goal of Dual Language: high academic achievement in both languages. Remember, DLE has been proven to provide high academic achievement, but only when we do it well.

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The 7 Sides of Dual Language Instruction™

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When The 7 Sides of Dual Language Instruction™ are properly implemented in Tier 1 instruction, programs can attain the first goal of Dual Language:

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The Equity Advantage of Small Groups - Dr. Catlin Tucker

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AI and blended learning can help educators create more equitable learning, experiences, but it necessitates mindset and skillset shifts.

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A Method and Model for Deeper Teaching and Learning

By: Erik M. Francis

If your understanding of depth of knowledge (DOK) is a little cloudy, you're not alone. This resource is your one-stop-shop for learning what it is and how to use it to provide learning experiences that are academically rigorous, socially and emotionally supportive, and student responsive.

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Deconstructing Depth of Knowledge

Depth of knowledge (DOK) has become a priority for many schools. But if your understanding of DOK is a little cloudy, you're not alone. This resource is your one-stop-shop for learning what it is, who it's for, and how to use and sustain it. Ultimately, you will discover how to plan and provide learning experiences that are academically rigorous, socially and emotionally supportive, and student responsive.

Learn how DOK is a different, deeper way of approaching teaching and learning.

Explore the different DOK levels and how they relate to instruction.

Understand DOK's relationship with standards and assessment.

Designate correct levels based on learning needs.

Acquire strategies for helping students engage with DOK on a deeper level.

Additional Information

Product Code: BKF960, EKF523

ISBN: 9781951075156

"Erik M. Francis masterfully explains the often-misunderstood Depth of Knowledge (DOK) framework. He walks you through the myths and misperceptions of the model and ultimately gives you the tools to apply it with fidelity in your classroom. This book should be on every educator's desk and should be the guide to deepening understanding for all students."

Richard M. Cash, Educator, Author, and Consultant, nRich Educational Consulting, Inc.

"Erik Francis's Deconstructing Depth of Knowledge demystifies the academic framework that has become the go-to for teaching and learning. His narrative guidance on the classroom application of Depth of Knowledge (DOK) empowers readers to apply this theoretical academic framework to real teaching and instruction. As Francis explains throughout, DOK isn't another 'thing' to do. Rather, it's a way of thinking and being that leads to effective teaching and learning. Accessible explanations paired with instructional exemplars make Erik Francis's book a must-have for all educators."

Katie McKnight, Ph.D. CEO and Founder, Engaging Learners

"Take a look at this book and you'll see why Erik Francis has been identified by Global Gurus as one of the world's Top 30 Education Professionals 2021. Deconstructing Depth of Knowledge: A Method and Model for Deeper Teaching and Learning presents teachers and administrators with a blueprint for the successful application of Depth of Knowledge with any student population. The how-to nature of the book makes it appropriate for veteran, mid-career, and novice teachers alike. This book is a must-have for educators at any level of teaching, from Kindergarten to grade 12."

Jaime Castellano, Educator, Author, and Professor, Florida Atlantic University

"In this book, Erik Francis has achieved what many educators have been craving-a demystification of Depth of Knowledge (DOK). To be fair to this excellent book, he has done so much more than that. In addition to providing quality descriptors, in practical terms, of each of the four DOK levels, he has shared how the DOK levels can serve as a method and model that informs how teaching and learning experiences can be developed, delivered, and deepened.

Tom Hierck, Educational Consultant and Author, Starting a Movement: Building Culture From the Inside out in Professional Learning Communities

"Forget the wheel! Deconstructing Depth of Knowledge takes what was once a mysterious and elusive concept and makes it practical, approachable, and doable. This resource explicitly examines what depth of knowledge is and is not in order to move beyond theory and misconceptions to create a clear framework of implementation for every educator, at every content, and at any level. Erik Francis has not only aligned his content to current research and pedagogy, but he also maintains the message throughout this book that all students can learn at high levels, and it is the work of educators that achieves this goal. Through this book, he actually shows us how."

Paula Maeker, Educator, Consultant, and Advocator for Learner-Centered Education

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If your understanding of depth of knowledge (DOK) is a little cloudy, you're not alone. This resource is your one-stop-shop for learning what it is and how to use it to provide learning experiences that are academically rigorous, socially and emotionally supportive, and student responsive.

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Enhance classroom learning with custom bilingual and multilingual educational programs from Sandra Mercuri Consultants. Discover our tailored solutions.

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Science of Reading, Tier 1 Instruction Intervention, reading research, spelling patters, sound wall, reading research, evidence-based, Foundations

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Open Middle Math Depth of Knowledge Matrix Archives - Robert Kaplinsky

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This smart, simple yearlong plan ensures that kindergartners write at or above a first-grade level by the end of the year.

This smart, simple approach ensures that kindergarteners write at or above a first-grade level by the end of the year. Master teacher Randee Bergen shares her yearlong plan for daily writing, providing complete lessons and tips for motivating all learners, managing writing time, and assessing children's work effectively and efficiently. Includes guided lessons for the whole group as well as individualized mini-lessons to support learners exactly where they need help.

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Teaching Writing In Kindergarten: A Structured Approach to Daily Writing That Helps Every Child Become a Confident, Capable Writer

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Amazon.com: Teaching Writing In Kindergarten: A Structured Approach to Daily Writing That Helps Every Child Become a Confident, Capable Writer: 9780545054003: Bergen, Randee: Books

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Translanguaging is one of the practices general education teachers can use in classrooms to support English learners as they access academic content.

It's the ability multilingual individuals possess to fluidly move between and simultaneously access two or more languages.

It's also a pedagogical approach to teaching that is gaining traction, and not only to help English learners, said Ryan Pontier, an assistant professor at Florida International University looking at the ways teachers draw on their languages as they are supporting students.

English-only instruction policies and mindsets through which translanguaging only serves as a scaffold for learning English can be barriers to ensuring true translanguaging happens in classrooms. But Pontier offers advice on how all teachers, including those who are monolingual, can support the practice.

To Pontier and other researchers, denying students the ability to translanguage denies them access to learning in a way that is representative of who they are as individuals.

"When we create intentional spaces and intentional tasks to be bilingual, to be multilingual, to produce something that draws on two or more languages, then what we're doing is we're actually pushing back against existing policies, we're becoming more transgressive. That's part of the trans nature of translanguaging," Pontier said.

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'Translanguaging Is What Bilinguals and Multilinguals Do': A Primer for Teachers

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What is translanguaging? Here is a guide to the practice, as well as examples for incorporating it in the classroom.

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2024 PaTTAN Literacy Symposium: SORing Into Structured Literacy

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The 2024 PaTTAN Literacy Symposium will provide educators in all capacities with the opportunity to deepen their knowledge and skills pertaining to evidence-...

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Emergent Academic Literacies for Dual Language Learners

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Cognitive Benefits of Being Bilingual for Young Children A Literature Review

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Phonemic awareness is the ability to hear and manipulate the smallest units of sounds (phonemes) in words. Many students with learning differences like dyslexia struggle with phonemic awareness. Because phonemic awareness is a foundational literacy skill, students who have trouble with this skill often have difficulty with reading.

Elkonin sound boxes can help students develop phonemic awareness by focusing on segmenting and blending the sounds in words. Segmenting is breaking a word apart into its individual sounds. Blending is putting the individual sounds together to say the word. Read on to learn how to use Elkonin sound boxes to teach these important skills.

K (Common Core ELA Literacy RF.K.2: Understand spoken words, syllables, and sounds)

1 (Common Core ELA Literacy RF.1.2: Understand spoken words, syllables, and sounds)

Gather materials. Give each student an Elkonin sound boxes printable and five counters, coins, tiles, or other small objects. For students who struggle with fine motor skills, consider using larger objects that are easier to handle.

Words with two sounds and two letters: it, no

Words with three sounds and three letters or with three sounds and four letters ending in a 'silent e': hop, cat, game

Words with three sounds and one or more digraphs (two letters that make one sound): fish, tooth, chime

Words with four phonemes and blends (two or more letters that blend together but that have separate sounds): crab, skip, trade

Words with five phonemes and blends: street, crust, blend

2. Model. Point to the set of boxes that has two squares. Explain that students will move one counter into one square for each sound they hear. Model how you'll do that by lining up two counters above the squares. Say the word it. Pull down one tile into the first square as you say /ĭ/ and the second tile as you say /t/. Then run your finger along the arrow underneath the box to blend the sounds back together: it.

3. Practice and give feedback. Invite students to practice another word with you. Depending on your students' needs, you might use the sound box with three squares and the word hit. Support students as they pull down one tile for /h/, one for /ĭ/, and another for /t/. Then blend the sounds back together to make hit.

Repeat the activity with several words that have two, three, four, or five sounds (as appropriate for your students). Provide feedback as students segment and blend the sounds.

For English language learners (ELLs): Hold up a picture of the word you are asking students to segment. Say the word and have students repeat it. (If the word is a cognate, say the word both in English and in the student's home language.) After students say the word in English, segment the sounds and then blend them together. By doing so, you'll help students build vocabulary by connecting the word with its meaning.

Understand: Why this strategy works

All students - and particularly students who learn and think differently - benefit from explicit instruction in literacy. The visual element of the sound boxes helps students to segment each sound separately. Similarly, moving the tiles and dragging their finger along the word helps to engage students in multisensory learning.

Elkonin sounds boxes are especially useful for ELLs. Learning English can be difficult for some students because there are sounds in English that may not exist in their home language. For instance, English has many more vowel sounds than Spanish. Elkonin sound boxes give students repeated practice with hearing unfamiliar sounds.

Share this resource with families to help them understand the importance of phonemic awareness and other phonological skills.

Research behind this strategy

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Elkonin sound boxes: An evidence-based literacy strategy

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Learn how to use Elkonin boxes to teach phonemic awareness. Get a printable of Elkonin sound boxes to help teach segmenting and blending.

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Effective Reading Strategies for Small Group Instruction with Dr. Matthew Burns

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Zaner-Bloser Kickstart | Number Sense Intervention, K-2

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Explore the complete number sense intervention kit for students struggling with primary core math curriculum.

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Maths – No Problem! Maths Mastery for Primary School Education

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Primary maths mastery articles by Dr. Yeap Ban Har

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Dr. Yeap Ban Har is one of the world’s leading authorities on Singapore maths and teaching maths mastery and an author for Maths — No Problem! blogs.

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Abstract

Language learning requires linguistic input, but several studies have found that knowledge of second language (L2) rules does not seem to improve with more language exposure (e.g., Johnson & Newport, 1989). One reason for this is that previous studies did not factor out variation due to the different rules tested. To examine this issue, we reanalyzed grammaticality judgment scores in Flege, Yeni‐Komshian, and Liu's (1999) study of L2 learners using rule‐related predictors and found that, in addition to the overall drop in performance due to a sensitive period, L2 knowledge increased with years of input. Knowledge of different grammar rules was negatively associated with input frequency of those rules. To better understand these effects, we modeled the results using a connectionist model that was trained using Korean as a first language (L1) and then English as an L2. To explain the sensitive period in L2 learning, the model's learning rate was reduced in an age‐related manner. By assigning different learning rates for syntax and lexical learning, we were able to model the difference between early and late L2 learners in input sensitivity. The model's learning mechanism allowed transfer between the L1 and L2, and this helped to explain the differences between different rules in the grammaticality judgment task. This work demonstrates that an L1 model of learning and processing can be adapted to provide an explicit account of how the input and the sensitive period interact in L2 learning.

Keywords: Second language learning, Sensitive period, Age of acquisition, Input‐based learning, Connectionist neural network, Language acquisition

1. Introduction

Linguistic input is critical for language learning. In first language (L1) acquisition, linguistic elements that occur more frequently are easier to learn (Ambridge, Kidd, Rowland, & Theakston, 2015; Bybee, 2006; Dąbrowska & Lieven, 2005; Marchman, Wulfeck, & Weismer, 1999; Phillips, 2006). However, the relationship between input frequency and second language (L2) learning is less clear. Several studies have reported that the amount of language input-as measured, for example, by years living in L2 environment-does not correlate highly with the acquisition of grammar and morphology in adult L2 learners who started learning the L2 at different ages (Andringa, 2014; DeKeyser, 2000; DeKeyser, Alfi‐Shabtay, & Ravid, 2010; Johnson & Newport, 1989; Lee & Schacter, 1997; McDonald, 2000; Oyama, 1978; Patkowski, 1980). Given that languages cannot be learned without linguistic input, these findings are counterintuitive and at odds with the notion that input plays an important role in L2 theories (N. C. Ellis, 2013; MacWhinney, 2008). This discrepancy in the role of the input suggests that differences exist in the mechanisms that are used by L1 and L2 learners, and this study examines whether these differences can be explained in a unified way.

Input effects in L2 learning are modulated by the critical or sensitive period, the time window approximately between birth and puberty during which language learning is most effective (Knudsen, 2004; Lenneberg, 1967). This effect is modulated by the age at which language learners begin learning the L2. As age of acquisition (AoA) increases, the ability to learn the L2 decreases (Flege, Yeni‐Komshian, & Liu, 1999; Johnson & Newport, 1989). While many of these AoA effects are found in explicit tasks, similar effects have been found in implicit tasks such as timed judgments (R. Ellis, 2005) and ERP studies (Weber‐Fox & Neville, 1996). Also AoA effects are found in L1 learning in deaf learners of sign language (Boudreault & Mayberry, 2006; Mayberry, 2010; Mayberry & Eichen, 1991) and international adoptees (Gardell, 1979; Gauthier & Genesee, 2011; Hyltenstam, Bylund, Abrahamsson, & Park, 2009). A wide range of social, motivational, input, and biological factors have been proposed to explain this reduction in learning ability (for a balanced review, see DeKeyser & Larson‐Hall, 2005). For these factors to explain the AoA effects, there needs to be a gradual accumulation of the negative impact of these factors as the learner gets older (e.g., motivation to learn the L2 decreases for each year of age). Understanding the mechanism that could explain the gradual reduction in L1/L2 learning in such diverse circumstances is an important goal for understanding language learning.

A classic study that investigated the sensitive period is that of Johnson and Newport ( 1989). The authors tested English morphosyntactic grammar knowledge in Korean and Chinese immigrants in the United States. They examined whether the English abilities of these L2 speakers could be predicted from the age at which they started learning English in immersion settings (3-39 years: age of acquisition, AoA), and years spend in the United States (7-30 years; length of exposure, LoE). The participants' L2 knowledge was assessed via a grammaticality judgment task, in which they indicated whether a given English sentence was grammatical (1a) or not (1b).

The farmer bought two pigs at the market

The farmer bought two pig at the market

The authors found that the performance dropped as AoA increased, showing that their ability to learn grammatical knowledge depended on the age at which they started learning the L2. However, they found no correlation between LoE and grammaticality judgment scores ( r =.16, p >.05) and this has been replicated in several other studies (DeKeyser et al., 2010; DeKeyser, 2000; Lee & Schacter, 1997; McDonald, 2000; cf. Flege et al., 1999). The lack of LoE effect is an important issue, as it contradicts the assumption that language ability should increase as more input is experienced (N. C. Ellis, 2013).

One reason why LoE effect was not observed in Johnson and Newport's ( 1989) study could be related to the variation among different rules used in test sentences. The authors examined grammatical knowledge of 12 different morphosyntactic rules (Table 1 ). For example, sentence (1b) violated the plural rule use that required adding -s to the plural noun "pig." Their data suggest that as AoA increased, the average grammatical knowledge dropped at different rates for different rules. Late learners performed worse with determiners and plural rules, whereas past tense and third‐person singular rules seemed to be easier to master. Similar rule‐specific effects have also been observed in several other studies (DeKeyser, 2000; Flege et al., 1999; Johnson, 1992; McDonald, 2000). Since their analyses collapsed the data over different rules, this within‐subject variation could have obscured the effect of between‐subject factors like LoE.

To understand the role that rule variation plays in sensitive period studies, we reanalyzed Flege et al. ( 1999) study, which was based on Johnson and Newport's ( 1989) original study but had a much larger sample of 240 Korean learners of English (compared to 46 participants in Johnson and Newport's study). To preview the findings, our analysis showed a significant effect of rule, which means that these learners were consistently better at judging grammaticality of some rules than others (consistent with rule differences in various L1/L2 studies; Leonard, Caselli, Bartolini, McGregor, & Sabbadini, 1992; McDonald, 2000; Mizumoto, Hayashibe, Komachi, Nagata, & Matsumoto, 2012; Rescorla & Reberts, 2002). One explanation for the rule variation is the differences in the frequency with which those rules occur in the input. Higher frequency rules are thought to yield better learning outcomes (Ambridge et al., 2015; N. C. Ellis, 2002; Lieven, 2010) and this predicts that L2 learners should be more accurate at judging the grammaticality of higher frequency rules. Another explanation is that rules that are similar across the L1 and L2 are easier to learn than those that are different (L1‐transfer/interference; Bernolet, Hartsuiker, & Pickering, 2013; Foucart & Frenck‐Mestre, 2012; Hartsuiker, Pickering, & Veltkamp, 2004; Ionin & Montrul, 2010; MacWhinney, 2005; Sabourin, Stowe, & de Haan, 2006). One challenge for transfer accounts is that there is no agreement about how to best measure L1-L2 similarity and it would be difficult to augment the Flege et al. analysis with an objective measure of L1/L2 similarity. Therefore, to contrast frequency and transfer accounts, we performed a corpus study to quantify the input frequencies for some of the rules in Flege et al.'s study and used these frequencies in the reanalysis to understand the differences in L2 learners' performance with different rules. If the frequencies positively predicted performance in grammaticality judgment task, it would support frequency‐based approaches. If this was not the case, then that would provide indirect evidence for alternative accounts like language transfer. Finally, we used a connectionist model of L1 language acquisition to see if we could model the findings in the reanalysis to understand how input frequency and language transfer might work in L2 language acquisition.

2. Corpus analysis

To make a grammaticality judgment, participants read a sentence and then classify it as either grammatical or ungrammatical. One way to make this decision would be to use knowledge about the transitions between words. For example, in the sentence The farmer bought two pig at the market, the transition between two and pig makes the sentence ungrammatical. One way to detect this ungrammatical transition would be to test if the frequency of the bigram two pig was below a threshold. However, since the raw bigram frequency can differ for different words (e.g., twenty‐three pigs is a rare grammatical bigram), it can be hard to distinguish grammatical and ungrammatical transitions based on raw bigram frequency knowledge. An alternative statistic that automatically adjusts for this is forward conditional probability (CP), which is the raw frequency of the bigram divided by the frequency of the previous word, for example, CP = frequency of twenty‐three pigs/frequency of twenty‐three. There is a lot of evidence that CPs can explain infants' language learning behavior (Aslin, Saffran, & Newport, 1998; Gomez & Gerken, 2000), as well as experimental results in children/adults (Jurafsky, 2003; Levy, 2008; Monaghan, Chater, & Christiansen, 2005; Thompson & Newport, 2007). Critically, there is evidence suggesting that L2 learners show a similar sensitivity to forward CPs as L1 learners in an on‐line task (Huang, Wible, & Ko, 2012). In this work, we explore whether forward CPs can explain the differences in rule performance in Flege et al.'s study. Our approach does not imply that people do not also extract other statistics such as backward CPs (e.g., frequency of twenty‐three pigs divided by frequency of pigs) or other n‐grams, and use them to aid language use (Bannard & Matthews, 2008; Chang, Lieven, & Tomasello, 2008; French, Addyman, & Mareschal, 2011; Huettig & Mani, 2016). The goal of this analysis is to provide some evidence that rule differences are related to at least one input frequency‐related measure.

To compute these statistics, we used child‐directed speech from CHILDES online child language database (MacWhinney, 2000) and adult input from a spoken subset of the Corpus of Contemporary American (COCA; Davies, 2010). From CHILDES, we used the mothers' utterances (a total of 591,762 in 32 North American corpora; Bates, Bretherton, & Snyder, 1988; Bernstein‐Ratner, 1984; Bliss, 1988; Bloom, 1970, 1973; Bohannon & Marquis, 1977; Brent & Siskind, 2001; Brown, 1973; Clark, 1978; Demetras, 1989; Feldman & Menn, 2003; Gleason, 1980; Hall, Nagy, & Linn, 1984; Higginson, 1985; Kuczaj, 1977; Morisset, Barnard, & Booth, 1995; Ninio, Snow, Pan, & Rollins, 1994; Peters, 1987; Post, 1994; Rollins, 2003; Sachs, 1983; Soderstrom, Blossom, Foygel, & Morgan, 2008; Suppes, 1974; Valian, 1991; van Houten, 1986; Warren‐Leubecker, 1982). The remaining corpora were Cornell, MacWhinney, McCune, McMillan, Snow, and Tardif (MacWhinney, 2000). These corpora consist of the recordings of mothers talking to their children of up to 8 years of age, as well as other adults/children (e.g., investigator, father, grandparents, siblings, uncles/aunts, babysitter).

Conditional probabilities depend on rule frequencies. To compute these frequency counts, we created search terms that were based on the items used to test grammaticality in Flege et al.'s study. For example, determiner (DET) knowledge was tested with an ungrammatical sentence like The boy is helping the man to build house, which requires the knowledge that the verb build must be followed by a determiner the before using the noun house. Thus to judge the grammaticality of the sentence, participants could use their knowledge about how likely a verb is followed by a determiner. To calculate this, we extracted the frequency of verbs followed by determiners (verb‐determiner) and the overall frequency of verbs (verb frequency) using the corpora tiers that were coded for syntactic categories and morphology. The DET rule CP was then calculated by dividing verb‐determiner frequency by the verb frequency and this tells us out of all verb uses in this corpus, what proportion were followed by a determiner. In addition to the determiner rule, we also collected CPs for four other rules: plural (PL), particle use in phrasal verbs (PAR), third‐person singular verb inflection (3PS), and past tense (PST). The PL CP was calculated by dividing the number of plural nouns by the total number of nouns, which provided a measure of how likely a plural rule was to be encountered in the input compared to other noun forms. The PAR CP was thus calculated by taking the frequency of verbs followed directly by a particle and dividing it by the total number of verbs, and this probabilistic knowledge could help to identify non‐adjacent particles as ungrammatical (e.g., The man climbed the ladder up carefully). The 3PS CP was calculated by dividing the number of verbs in third‐person singular form by the total number of verbs, and this could help identify how likely a 3PS form was to be encountered. The PST CP was calculated by dividing the number of past tense verbs by the total number of verbs, and this provides information about how likely past tense was in general. Table 2 shows the implemented CLAN search terms (MacWhinney, 2000) and the corresponding raw frequency for each rule (number of utterances that matched).

Table 3 shows rule conditional probabilities for the same rules. It also includes rule CPs extracted from a subset of the COCA corpus to show that the results are consistent across different corpora. The correlation between rule CPs in the CHILDES and COCA corpora was high ( r =.74), which means that the frequency of these five rules was similar across both children‐ and adult‐directed speech. This correlation is due to the fact that the CPs for the DET/PL rules are higher than the 3PS/PST rules in both corpora, but the rank order within these rules is not always consistent. Since the COCA corpus was a transcription of television news programs (e.g., discussions of the Peacemaker missile system), we view this as being less typical of the input that L2 learners are generally exposed to in day‐to‐day settings. Since the CHILDES corpora include conversational speech between adults and other adults, as well as children of up to 8 years of age, we view them as a better measure of the frequent word and structures that L2 learners are likely to use and know, and hence the following analyses used the rule CPs from the CHILDES corpora only.

This corpus analysis has provided two measures of frequency for each rule: raw frequency and CP. In the next section, we will test these different measures to see which best explains the rule differences in the Flege et al. study. If there is a significant positive effect of either frequency measure, then that would suggest that the 240 participants in that study had better knowledge of rules that were frequent in the input.

3. Flege et al. (1999) reanalysis

Flege and his colleagues investigated the knowledge of English grammar in 240 Korean immigrants living in the United States who had migrated at the ages between 1 and 23 ( M = 12, SD = 5.9). At the time of testing, their average age ranged from 17 to 47 ( M = 26, SD = 6). All participants had lived in the United States from 7 to 30 years ( M = 14.6, SD = 4.6). Half of the participants were males or females and different AoA groups had representative sample of participants with different LoE (Table 4 ).

The authors tested morphosyntactic knowledge for 10 rules using a grammaticality judgment test consisting of 144 sentences. The items were designed so that each grammatical sentence had an ungrammatical counterpart that violated a certain grammar rule (see Table 1 for examples). The participants heard a recorded sentence and were required to indicate if it was permissible in the English language. Consistent with Johnson and Newport's ( 1989) results, Flege et al. ( 1999) found that the scores for different rules varied with AoA, but their analysis involved separate anova models for each rule. The novel feature of our reanalysis is to include rule‐related predictors in the model to factor out rule variation from individual variation in LoE and AoA. In addition, we used logistic mixed effects models that could predict binary grammatical judgments for individual sentences while factoring out participant and test item variation. Since our goal was to examine how input variation influenced the acquisition of different L2 rules, we excluded the data from native English speaker and only used the data from the five rules (DET, PL, PAR, 3PS, PST) for which we had objective and comparable search terms. Since grammatical sentences must conform to multiple grammatical rules, we used the ungrammatical test items, because correct rejection of these rules is more likely to relate to the rule that was used to make the sentence ungrammatical. There were eight test sentences for each rule (except for PAR which only had 6 items) and overall there were 9,120 judgments for the 38 test items over 240 participants.

To replicate the earlier studies that found no effect of LoE, we first analyzed the data without including any rule‐related predictors. Grammaticality judgments (grammatical= 1, ungrammatical= 0) were predicted by a logistic mixed model with AoA crossed with LoE (all predictor variables were centered) and participant and test sentences as random effects. The maximal model that converged contained AoA crossed with LoE as random slopes for test sentence (R version 3.0.2; Barr, Levy, Scheepers, & Tily, 2013). Likelihood‐ratio tests were used to compare models and a chi‐squared statistic for the comparison was used to compute p‐values. The same approach was used for all the models in this paper. As seen in Fig. 1 A, there was a significant effect of AoA which suggests an age‐related reduction in L2 learning ability (β = −0.2, SE = 0.02, χ ²(1)= 65.98, p < .001). There was no effect of LoE ( p = .17) and no interaction between the two variables ( p = .25). Thus, we find that the years of input is not a strong predictor of grammaticality judgments when the variability between rules is treated as unexplained variance.

Next, we added rule as a categorical factor (fully crossed with AoA and LoE) to see if L2 learners showed consistent patterns in their knowledge for certain rules. The maximal model that converged contained no random slopes. There was a significant negative effect of AoA (β= −0.161, SE = 0.02, χ ²(1) = 177.51, p < .001), a positive effect of LoE (β= −0.001, SE = 0.03, χ ²(1)= 4.13, p = .042), and a negative effect of rule (χ ²(1)= 24.28, p < .001). There was a marginal interaction between AoA and LoE (β= 0.003, SE = 0, χ ²(1)= 3.08, p = .079). There was also a significant interaction between AoA and rule (χ ²(1)=61.78, p < .001). Finally, there was a three‐way interaction between AoA, LoE, and rule (χ ²(1)= 13.56, p = .0088). This analysis demonstrates that participants with different AoA and LoE show consistent differences between the rules that they are tested on (e.g., judgments of past tense rule items were consistently better than judgments of determiner rule items). When this rule‐related variability was factored out, then LoE showed a significant positive effect, where more years of input led to better knowledge of English grammar. Thus, the weak nature of LoE effects in previous studies could be due to the fact that earlier analyses treated rule variation as unexplained variance. The variation due to rule can be clearly seen in Fig. 1 B, where we split AoA into early learners (<12 years) and late learners (>12 years, both 120 participants). We used 12 years, because this is where a non‐linearity occurs in the data (Flege et al., 1999), but we make no claim about the special role of this particular age.

The above analysis suggests that there are consistent differences among the rules, but since rule is a factor, each level of rule is treated as an arbitrary category and the analysis provides no explanation for these rule differences. One possible explanation of these rule differences is that participants rely on the knowledge of the raw frequency of the categories at the critical point in the test utterances. For example, knowing how frequently a verb is followed by a preposition can help to identify the error in the PAR rule item The horse jumped the fence over yesterday. To test this hypothesis, we tested a fully crossed model with categorical rule replaced by centered frequency for the adjacent categories at the critical point. The maximal model that converged contained random slope of AoA for test sentence and no slopes for participant. There was a significant negative effect of AoA (β = −0.2, SE = 0.02, χ ²(1)= 68.8, p < .001), a marginal effect of LoE (β = 0.05, SE = 0.02, χ ²(1)= 3.7, p = .055), and a negative effect of frequency (β = −0.00001, SE = 0.000003, χ ²(1)= 4.96, p < .03). There was a marginal interaction between AoA and LoE (β = −0.006, SE = 0.004, χ ²(1)= 2.99, p = .08). There was also an interaction between AoA and frequency (β = −0.0000005, SE = −0.0000002, χ ²(1)= 6.29, p = .012). Finally, there was a three‐way interaction between AoA, LoE, and frequency (β = −0.00000005, SE = 0.00000003, χ ²(1)= 3.84, p = .05). This analysis suggests that the rule differences in judgment behavior can be explained by a frequency measure. But unlike the previous model with rule as a factor, this model found only a marginal effect of LoE. Furthermore, since raw frequency will vary with the frequency of the component categories and the size of the corpus, we will test whether forward CPs, which are less sensitive to these factors, can explain this rule variation.

The next model included forward rule CP fully crossed with AoA and LoE. Rule CPs are computed from the raw frequencies divided by the previous category and hence they can vary between 0 and 1 (regardless of the frequency of the corresponding categories or the corpus size). The maximal model that converged contained random slopes for rule CP for participants and random slopes for AoA for test sentence. There was a significant negative effect of AoA (β = −0.2, SE = 0.02, χ ²(1)= 68.8, p < .001), a positive effect of LoE (β = 0.04, SE = 0.02, χ ²(1)= 4.17, p = .04), and a negative effect of rule CP (β = −21.3, SE = 3.76, χ ²(1)= 20.1, p < .001). There was a significant interaction between AoA and LoE (β = −0.01, SE = 0.004, χ ²(1)= 4.94, p = .03). There was also a marginal interaction between AoA and rule CP (β = −0.5, SE = 0.33, χ ²(1)= 3.37, p = .07). Finally, there was a three‐way interaction between AoA, LoE, and rule CP (β = −0.1, SE = 0.04, χ ²(1)= 5.93, p = .015). This shows that as AoA increased, the weakening effect of LoE affected higher CP rules more that lower CP rules.

One puzzle in the L2 literature is that years of studying an L2 do not seem to positively predict knowledge of the L2 (DeKeyser, 2000; DeKeyser et al., 2010; Johnson & Newport, 1989; Lee & Schacter, 1997; McDonald, 2000). We replicated this finding (non‐significant LoE) in our first model without any rule‐related predictors. Furthermore, a model that included raw frequency did not yield a significant effect of LoE, suggesting that this predictor did not factor out rule variations sufficiently to be able to see the effects of LoE. But when rule was added as a factor or as rule CP, we found a significant positive effect of LoE, where performance improved with more linguistic exposure. In addition, while all the models exhibited a sensitive period effect (a reduction in grammatical knowledge with increased AoA), only the rule CP model exhibited a significant interaction between LoE and AoA, where late learners benefitted from the input less than early learners. We suggest that previous studies did not find positive effects of LoE or interactions of LoE with other factors, because they did not fully factor out variation between rules.

In addition to clarifying the effect of AoA and LoE, these rule‐related predictors in the model suggested that some rules were consistently easier than other rules, regardless of the test sentence they were in or participant differences. Both the raw frequency and rule CP models suggest that these rule differences are due to a negative relationship with frequency. This conflicts with theories of L1 and L2 learning which argue that higher frequency should lead to greater accuracy (Ambridge et al., 2015; N. C. Ellis, 2002) and this work will attempt to explain this discrepancy. To better understand this negative effect, we need to determine which measure of frequency provides the best account of the data. One way to compare these models is with R ², which is the variance explained by each model (Johnson, 2014; Nakagawa & Schielzeth, 2013). The model without rule CP explained about 21% of the variance. The model with raw frequency explained an extra 4% ( R ² = .25) and the rule CP model explained about 9% more ( R ² = .30). Since the rule CP model explained the most variance and uses a measure of frequency that is less dependent on word and corpus properties, we will use rule CP as our proxy for frequency in L2 learning.

The rule CP model revealed a significant three‐way interaction between AoA, LoE, and CPs. This indicates that the weaker effect of LoE in later AoA learners impacted higher CP rules more than lower CP rules. Specifically, Fig. 1 B shows that the high CP rules DET and PL have a strong positive LoE slope in early AoA learners, but the slope is smaller in late learners. However, the slopes of lower CP rules like PST and 3PS were less affected by AoA. This suggests that late AoA learners have trouble using the high frequency of higher CP rules to acquire them better.

In sum, our reanalysis of Flege et al.'s data suggested a complex set of mechanisms in L2 grammatical learning. These learners showed a sensitive period effect (negative effect of AoA). In support of frequency‐based approaches (e.g., N. C. Ellis, 2002), we found that the amount of input (LoE) had a positive effect on L2 learning, but this was reduced in late learners. However, frequency‐based approaches cannot explain the negative effect of rule CP, where frequent noun‐based rules were associated with lower accuracy scores than less frequent verb‐based rules. Since each of the 240 participants was tested on each rule, the difference in the rules cannot be easily attributed to between‐participant differences in motivation, social factors, or biological factors. A likely cause of the rule differences is transfer from L1, since Korean does not have determiners and uses plural marking less than English. Support for the transfer account can be found in Ionin and Montrul ( 2010), who found that Korean learners of English had more trouble learning the generic interpretation of English determiners compared to matched Spanish learners, and this is presumably because Spanish speakers could use determiners in their L1 to enhance their learning of English. However, the Korean learners also learned third‐person singular verbs fairly easily even though the Korean language does not mark this distinction, so it is not obvious what kind of transfer mechanism could explain the learning of this rule. One possible account of language transfer are connectionist learning mechanisms that can encode similarity structure using distributed representations (Twomey, Chang, & Ambridge, 2014). In the next section, we examine whether a connectionist model is able to explain the findings in our reanalysis.

4. A connectionist model of the acquisition of morphosyntactic rules in L2

In the present work, we developed a computational model of L2 language acquisition and sentence processing and used it to examine the results observed in our Flege et al. reanalysis. The model is based on the connectionist model of L1 learning and processing called the dual‐path model (Chang, 2002). The model has several features that are relevant for its application to this dataset. First of all, the model has been shown to be able to learn abstract English grammatical constraints like those that are tested in Flege et al.'s study (Chang, Dell, & Bock, 2006). Second, the model can learn typologically different languages (Chang, Baumann, Pappert, & Fitz, 2015) and, in particular, it has been shown to be able to learn and explain various Japanese phenomena (Chang, 2009), which is a verb‐final case‐marked language like Korean. Finally the model uses linguistic input to make small changes to its morphosyntactic knowledge within a limited capacity memory and this means that the knowledge that it learns for different rules may compete with or support learning of new rules (Fitz, Chang, & Christiansen, 2011; Twomey et al., 2014).

To simulate the environment of L2 learning at different ages, we first trained the dual‐path model on Korean‐like L1 input until it reached adult‐like performance. The weights in the Korean model were saved after every 3,000 epochs (1,000 epochs represented one human year) and were used as the starting points for the models learning English as an L2. By varying the starting point, we simulated children who had different amounts of Korean knowledge before moving to an English‐speaking environment at different ages (AoA). Since the same model weights are used to learn both languages, the model instantiates the idea that shared systems are used for both L1 and L2 languages (Hartsuiker & Pickering, 2008; Hartsuiker et al., 2004; Schoonbaert, Hartsuiker, & Pickering, 2007). This shared system assumption combined with the model's learning mechanism is consistent with evidence for transfer between L1 and L2 in various tasks (e.g., structural priming; Chang et al., 2006).

4.1. The Korean L1 and English L2 input environment for the models

Both the Korean and English languages consisted of simple intransitive, transitive, and dative structure sentences. The languages were composed of 40 words: eight animate nouns, eight inanimate nouns, six transitive, six intransitive, and six dative verbs. The Korean language included function words/morphemes (particles) that denoted case (e.g., nominative ka, accusative ul, dative ey key) and verb endings (e.g., ‐da). The English language contained morphemes to mark tense (‐ed, ‐ing), third‐person singular verb inflection ( ‐ss), noun number ( ‐z, this letter was chosen to differentiate it from third‐person singular inflection), and determiners ( a, an, the, this, that, two, three, many, several) with the appropriate plural counterparts. To test particle movement rules, the grammar also contained two prepositions for creating phrasal verbs (down, up).

To train the models, sentences were paired with corresponding messages. Intransitive sentences had one argument Y in the message that mapped onto the subject slot. Transitives had an agent X and a patient Y argument that mapped onto the subject and object slots, respectively. Finally, datives had an agent X, a patient Y, and a goal Z argument that mapped onto the subject, object, and indirect object slots (Table 5 ). Each argument was made up of a concept (e.g., CAT) and features that helped to structure the noun phrase (e.g., Y = CAT, THREE,DIST). There was a special argument for lexical action information (e.g., A = DANCE). In addition, the message contained event‐semantics (e.g., E = PROG,YY), which had information about tense and aspect of the event. There were two possible tenses (present, PAST) with two possible aspects (simple, PROGressive). Present tense and simple aspect were considered default and had no event‐semantic features. The event‐semantics also contained features that encoded the number of roles that were required to describe a given event (XX, YY, ZZ). Both Korean and English languages shared the same meaning system but used different words in the lexicon to express the message. For simplicity, the Korean content word vocabulary was created by adding the letter "k" to the beginning of the English content words (the labels play no role in the model's behavior).

The language had features that captured some of the constraints in different rules in English and Korean (Table 6 ). Each noun argument in the message had a kind feature and a number feature that helped create noun phrases. The kind feature could be DEFinite, INDEFinite, PROXimate, or DISTal. The number feature could be SINGular, TWO, THREE, PLURal. All kind features were equally frequent and the singular feature was eight times more frequent than other number features. If the argument had PLUR number feature, then the noun was followed by -z (plural morpheme). PLUR nouns were preceded by the word those if the kind feature was DIST, the word these if the kind feature was PROX, the number word (e.g., two) if the kind feature was DEF, the word the if the number feature was PLUR, and nothing if the kind feature was INDEF. If the number feature was SING, then DEF mapped to the word the, INDEF mapped to the word a, PROX mapped to the word this, and DIST mapped to the word that. If the kind feature was INDEF, then the TWO number feature mapped to the word several and the THREE number feature mapped to the word many (otherwise TWO mapped to the word two and THREE mapped to the word three). If the kind feature was INDEF and number was SING and the following noun started with a vowel, then the article a was changed to the word an. If the noun was a liquid or mass noun like sugar, milk, water, or coffee in the plural form, then the article was omitted. In the Korean language, there were no articles except for kthis and kthat, which were signaled by the PROX and DIST features. Number features like TWO mapped to ktwo and THREE mapped to kthree in prenominal position, but there was no other plural marking. The complex nature of English noun phrase rules is one possible reason that Korean learners of English have trouble judging the grammaticality of DET and PL rules.

There were also rules for verb construction that depended on the event‐semantic features. If the features had PROG, then the verb was followed by -ing and preceded by the word is if the feature PRES was active or the word was if the feature PAST was active. If the aspect was simple, then -ed was added after the verb for the PAST feature or -ss for the PRES feature. If the subject was plural, then the word is was changed to the word are, the word was was changed to the word were, and the -ss marking was removed. In Korean, simple PRES verbs were followed by -da, simple PAST verbs by -eoss -da, PROG PRES verbs by -iss -da, and PROG PAST verbs by -iss -eoss ‐da. In English, there were several phrasal verbs. There were intransitive verbs give‐up and show‐up that combined dative verbs give and show with the prepositions up. There were two transitive verbs turn‐down and break‐down that combined intransitive verbs turn and break with the preposition down. In Korean, these phrasal verbs were treated as separate verb forms. Therefore, the Korean model will have to learn that in English, verbs like turn can have two forms with different syntactic constraints and this should complicate the learning of the PAR rule. Although English and Korean have different rules for verbs, they are less different from each other in this respect.

The grammar was created to match the order in which the five rules occurred in the corpus analysis in terms of their CPs (Table 7 ). The CPs for these rules in the model's training set were extracted using the same formula as in the corpus analysis. Since the language was a simplified version of English, the model input CP values only match the relative order of CPs in the human data (correlation between the two is .95).

To train the models, 10 randomly generated training sets of 20,000 message‐sentence pairs were created for each age of L2 acquisition. This created 10 model subjects for each different AoA group. The message was excluded from 25% of the training pairs to increase the syntactic nature of the learned representations.

4.2. Dual‐path architecture

The dual‐path architecture is a connectionist architecture that can learn abstract rule‐like syntactic representations that interact with messages in sentence production (Chang, 2002). It has two pathways; sequencing pathway for learning sentence structure (lower half of Fig. 2 ) and meaning pathway for learning word to role mappings (upper half of Fig. 2 ). To adapt the model for L2 learning, the input and output layers have word units for the words in both English and Korean languages. Otherwise, the other features of the model are similar to the previous L1 versions of the dual‐path model.

The sequencing pathway is based on a simple recurrent network (SRN) architecture (Elman, 1993). The network attempts to predict the next word in a sequence from the previously heard word. The previous word is an activation pattern in the Previous Word (Input) layer. Activation spreads from the Previous Word layer to the Hidden layer via a CCompress layer and then from the Hidden layer to the Produced Word layer via another Compress layer. The function of the two compress layers is to force the model to form grammatical categories instead of learning individual word‐to‐word mappings (Elman, 1993). The Hidden layer learns and stores representations (activation patterns) that maps between the categories of the previous word and the next word and it also receives input from a Context layer that holds a copy of the Hidden layer's activation at the previous time step (dotted arrows in Fig. 2 ). This allows the model to learn longer distance dependencies between elements (Christiansen & Chater, 1999b).

The model learns through back‐propagation of error (Rumelhart, Hinton, & Williams, 1986). At the beginning of the training, the weights are initialized randomly with a range of 0.5. First, activation spreads through the network and generates a prediction about the next word in a sentence. The mismatch between the predicted Produced Word activations and the target is called error, and it is used to make small changes in the connection weights that generated the prediction. This error signal is then propagated back through the network adjusting the connection weights between all layers so that the predicted output better matches the target. Using this mechanism, the model learns weights that encode the structure of the language (all solid arrows in Fig. 2 ).

The sequencing system interacts with the message information in the meaning system. The message is instantiated in weights between a set of Role units and the Concept layer (Role‐Concept bindings). When the message contains Y = DOG, the Y role unit is linked to the concept DOG with a weight of 6 (thick black lines in Fig. 2 ). Since the Concept layer is linked to the Produced Word layer, the model can learn to activate a particular word when the appropriate concept is activated (concept DOG would activate kdog in Korean and dog in English). To allow the sequencing system to know which roles are present in the message, the Event Semantics layer has units that signal the number of roles. For example, if this layer had XX and YY units activated, that would signal to the sequencing system that it should activate the agent X Role unit after the first determiner (since English agents tend to occur early in sentences). In contrast, the Korean model would learn to activate the agent X role in sentence initial position and would also learn to activate the subject particle ka afterward to mark its role. In addition, the meaning system has a comprehension message, which tells the model the role of the previous word in the sentence, which helps the model produce structural alternations (e.g., active/passive). This system maps the Previous Word layer to the CConcept layer, which is linked to the CRole layer with a reverse copy of the Role‐Concept links (thick black lines on left side of Fig. 2 ). There is also a CRole Copy layer that helps the model keep track of the roles that have been processed.

In the present work, we apply the dual‐path model to explain L2 behavioral data in the Korean L2 English learners in the Flege et al. study. In the present work, we train models using Korean language as an L1 and then expose them to English as an L2. Consistent with the claim that L1 and L2 involved the same learning mechanism, we have kept the L2 version of the dual‐path model as similar as possible in its architecture and parameters to L1 English versions of the model (e.g., Twomey et al., 2014).

4.3. Evaluating the model's English grammatical knowledge

To gauge the overall learning of the language at different AoAs in the 10 models, we assessed the word prediction accuracy every 3,000 epochs using 200 randomly generated test sentences. To see how successfully the model learned the grammatical constraints in the rules in the Flege et al. study, we also examined its ability to distinguish grammatical and ungrammatical versions of the five rules in our reanalysis (DET, PL, PAR, 3PS, PST). Each test item had a matched grammatical and ungrammatical version (Table 8 ), and there were 100 items for each of the five rules.

To test the model's knowledge of each rule, sum of squares prediction error (the difference between the actual activation and the target activation for the word layer) for the target word at the part of the sentence where the grammatical and ungrammatical sentences differed was computed for both versions. For example, to test DET rule in the sentence a boy touch -ed the apple, the error of predicting the article the was compared to the error of predicting the word apple when the article was omitted as in a boy touch -ed apple. For each rule, the average sum of squares error (SSE) was calculated for both the grammatical and ungrammatical items. Then a rule proportion measure was computed by dividing the average SSE of ungrammatical sentences by the sum of the average SSEs for both grammatical and ungrammatical sentences. Since error levels should be larger for ungrammatical sentences than grammatical sentences, higher rule proportion scores express better rule knowledge. If the model has not developed strong expectations about whether the verbs tend to be followed by determiners or not, then SSEs for both should be similar and rule proportion should be close to 0.5. Rule proportion in the simulations approximated the grammatical judgment accuracy measure in the Flege et al.'s study and our goal is to see if the model shows similar results to those observed in the reanalysis of their data. It is known that in ERP studies (e.g., Weber‐Fox & Neville, 1996), the brains of L2 learners generate mismatch signals and this means that there is evidence that implicit prediction error signals like SSE are generated in their brains and could be used to make grammaticality judgments. However, since L2 tasks vary in their dependence on implicit and explicit knowledge (R. Ellis, 2004, 2005, 2006), different tasks might have different assumptions about the way that implicit signals like SSE are used to make behavioral choices.

4.4. Model simulations

We present several different simulations that attempt to approximate the L2 results in the Flege et al.'s reanalysis. Our first simulation tested whether the model's activation function could create the age‐dependent sensitive period. The second simulation manipulated the sensitive period by reducing the model's learning rate after puberty. The third simulation introduced different learning rates for the lexical and syntactic parts of the model. Finally, the fourth simulation implemented a model that received both English and Korean input to mimic the learning environment of many L2 learners.

4.4.1. Simulation 1: Activation function‐based sensitive period effects

The activation function that is typically used in back‐propagation has been argued to create sensitive period effects (Elman, 1993; A. W. Ellis & Lambon Ralph, 2000; Marchman, 1993; Mermillod, Bonin, Méot, Ferrand, & Paindavoine, 2012; Munakata & McClelland, 2003; Zevin & Seidenberg, 2002). In these models, activation is spread forward in the network and the net input for a unit is the weighted sum of input activations. The net activation is passed through a logistic/sigmoid activation function to create the output activation. When the weighted sum input is 0, the logistic output activation will be 0.5. On the backward pass, the output activation is compared to the target to compute the error and this error is back‐propagated through the network to change the weights. The first step of this back‐propagation involves the computation of the derivative of the activation function. For the logistic activation function, the derivative is highest when the output activation is near 0.5 (derivative = o (1− o) when o is the output activation). The derivative of the activation function modulates the effect of error so that the same amount of error will have a larger effect on the weights when the weighted sum input is close to 0. When the weights are small, the weighted sum input to a unit will be small and the large derivative will allow relatively large weight changes. Typically weights in these models are initialized to small values early on and hence these models should be more sensitive to input early in development compared to later in the development. Knowledge learned early in L2 learning can therefore become entrenched and can inhibit later L2 learning (e.g., N. C. Ellis, 2013; A. W. Ellis & Lambon Ralph, 2000; Monner, Vatz, Morini, Hwang, & DeKeyser, 2013).

In previous versions of the dual‐path model (Chang, 2002), the output layer used a soft‐max activation function, which creates a winner‐take‐all bias, so that the model prefers to select only one word. To test whether the logistic activation function can create a human‐like L2 sensitive period, the first simulation used this activation function for the output layer and a constant learning rate throughout the training. To aid the comparisons with the human data, the model's age was represented as the number of training trials divided by 1,000 (e.g., 1 model year refers to 1,000 training trials or epochs). We applied a learning rate of 0.1 since this level allowed the model to learn Korean to an adult level within five model years.

To examine the AoA effects, we looked at the overall word accuracy of the Korean models that started learning English at different AoAs. Fig. 3 A shows the percentage of correctly predicted words in the Korean (gray line) and English (black lines) models that started learning the L2 at different ages. Later AoA models appeared to learn English slower, but reached similar accuracy levels after 20 model years.

To explore the model's grammatical knowledge with different rules over development, a mixed effect model was used to predict rule proportion scores with AoA, LoE, and rule CP fully crossed (Fig. 3 C). All simulations contained model subject as a random intercept with random slopes for LoE crossed with Rule CP. The analysis revealed a negative effect of AoA (Fig. 3 B), confirming that later AoA models performed worse than early AoA models (β = −0.01, SE = 0.001, χ ²(1) = 65.8, p < .001). LoE effect showed that longer exposure to language resulted in better overall scores (β = 0.02, SE = 0.001, χ ²(1) = 73, p < .001). There was a positive main effect of rule CP showing that the models performed better with the higher probability rules (β = 0.14, SE = 0.01, χ ²(1) = 73.2, p < .001). There was a two‐way interaction between LoE and rule CP, where higher probability rule benefited more from increasing LoE (β = 0.008, SE = 0.002, χ ²(1) = 16.6, p < .001). Finally, a three‐way interaction between AoA, LoE, and rule CP showed that this effect became stronger as AoA increased (β = 0.001, SE = 0.0003, χ ²(1) = 4.07, p = .04).

In sum, Simulation 1 showed a negative effect of AoA and this is consistent with connectionist models where the logistic function creates an age‐dependent reduction in learning ability (A. W. Ellis & Lambon Ralph, 2000; Zevin & Seidenberg, 2002). However, the results of this model are different from those in Flege et al.'s ( 1999) data in several important ways (compare Fig. 1 A vs. Fig. 3 B). The sensitive period created by the logistic function is smaller than the one in human learners. Connectionist models learn from the input and therefore there is large LoE effect in the model. Late AoA human learners in Flege et al.'s data also showed lower sensitivity to LoE (Fig. 1 B), but the present model shows no interaction between LoE and AoA (Fig. 3 C). Furthermore, the human results showed a negative effect of rule CP, whereas the present model shows a positive effect. Finally, there is evidence that the sensitive period limits ultimate language attainment even with extensive input (DeKeyser & Larson‐Hall, 2005), but the present model is able to catch up with early learners and hence does not match this aspect of human learning. For example, one of the participants in Flege et al. study scored only 58% judging the grammaticality of PL rule use even after 25 years of English input (model is closer to 90% at 20 model years). So while the logistic function can create age‐dependent changes in learning, it does not capture the full behavior of L2 learners.

4.4.2. Simulation 2: Stretched Z learning rate function for the sensitive period

Simulation 1 showed that activation function was not sufficient to create a human‐like sensitive period. To make the effects stronger, we directly changed the model's learning rate as it aged. There is evidence that the sensitive period has a stretched Z function (Birdsong, 2005; Flege et al., 1999; Granena & Long, 2013; Johnson & Newport, 1989; Mayberry & Eichen, 1991), where performance is high initially, but then declines gradually and is followed by a period of slower learning. These developmental changes were incorporated into the model by keeping the learning rate high (0.1) until model year 10, after which, the learning rate dropped to 0.025 over the following 6 model years (Fig. 4 ). With this learning rate function, later learners will have a lower learning rate in development and that might keep them from changing their Korean representations to the extent that would allow them predict English sentences with high accuracy.

Also, since the previous L1 work with the dual‐path model used the soft‐max function on the output layer (Chang, 2002), the following simulations will use that activation function to increase the similarity between the model's account of L1 and L2 learning.

Fig. 5 A shows the percentage of correctly predicted words in Korean (gray line) and English (black lines) models that started learning L2 at a different age. While all models reached high scores with enough training, the speed with which they achieved it was slower in later AoA models.

Statistical analysis confirmed that there was a significant negative effect of AoA (Fig. 5 B), indicating that later AoA models had greater difficulty in distinguishing grammaticality (β = −0.02, SE = 0.001, χ ²(1) = 94.4, p < .001). There was a positive effect of LoE (β = 0.01, SE = 0.001, χ ²(1) = 101, p < .001), which showed that language exposure increased the models' accuracy, and a positive effect of rule CP (β = 0.07, SE = 0.007, χ ²(1) = 51.6, p < .001), which demonstrated that they performed better with higher CP rules (Fig. 5 C). There was a positive two‐way interaction between AoA and LoE, showing that later AoA models benefited from increasing LoE more that early AoA models (β = 0.0005, SE = 0.0001, χ ²(1) = 11.6, p < .001). There was also a positive interaction between LoE and rule CP, showing higher CP rules were more sensitive to increasing LoE than lower CP rules (β = 0.005, SE = 0.0001, χ ²(1) = 20.3, p < .001). Finally, a three‐way interaction between AoA, LoE, and rule CP showed that this effect became stronger as AoA increased (β = 0.0005, SE = 0.0001, χ ²(1) = 11.9, p < .001).

The reduction in the learning rate created a stronger sensitive period effect that resembles the human data more closely (compare Fig. 1 A and ​and5B). 5 B). However, like Simulation 1, the late learning models acquired the language to near native levels (Fig. 5 A) and the effects of rule CP and the interaction between LoE and AoA were in the opposite direction to the corresponding effects in the human data.

4.4.3. Simulation 3: Lexical and syntactic learning rates

Cognitive and neurobiological explanations of sensitive period often focus on differences between lexical and syntactic learning (Paradis, 2004; Ullman, 2015). This distinction is supported by the studies of feral children like Genie, who started learning her first language at 13 and was able to learn new words faster than other children in the same MLU stage of development, but never fully mastered English grammatical knowledge (Curtiss, Fromkin, Krashen, Rigler, & Rigler, 1974; Curtiss, Fromkin, Rigler, Rigler, & Krashen, 1975; Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974). In addition, Singleton and Lengyel ( 1995) have argued that there is no sensitive period for vocabulary learning in either L1 or L2 language and in some cases, L2 learners outperform native learners in word learning tasks (Kaushanskaya & Marian, 2009). There is also evidence that late learners show N400 signatures for newly learned L2 words even after only 14 h of instruction (McLaughlin, Osterhout, & Kim, 2004). Weber‐Fox and Neville ( 1996) found reduced syntactic P600 effects in late learners (AoA > 11) for phrase structure, but lexical N400 effects were present for both early and late learner when a word appeared in a position that was not expected in terms of meaning. These studies suggest that AoA has a greater negative impact on syntactic learning than lexical learning.

To examine this hypothesis in the model, we incorporated separate learning rates and varied them independently for the lexical and syntactic learning weights in the model. The lexical learning system included the connections between Concept and Produced Word layers and the connections between Hidden, Compress, and Produced Word layers (gray arrows in Fig. 2 ). These parts of the model were responsible for selecting the right output word, whereas the remaining parts of the model were involved in learning structural regularities (black arrows in Fig. 2 ). The syntactic learning rate remained fixed at 0.1 for the first 10 model years and then was reduced to 0 across the following 6 years. The learning rate in the lexical learning part of the system remained fixed at 0.1 throughout training (Fig. 6 ).

The focus on the distinct properties of the lexical and syntactic systems is similar to Ullman's ( 2001) declarative/procedural theory. In his theory, syntactic rule learning depends on implicit procedural learning and this is in agreement with our model, which only implements implicit statistical learning (Chang, Janciauskas, & Fitz, 2012). However, Ullman's theory argues that lexical learning involves declarative systems. In our model, long‐term lexical knowledge is also learned though procedural learning. The fact that procedural learning is involved in lexical learning is supported by studies showing that word‐based repetition priming is present in anterograde amnesic patients, even though their declarative learning systems are damaged (Gordon, 1988; Mayes & Gooding, 1989; Schacter & Graf, 1986). This type of priming has been argued to reflect implicit learning processes (Oppenheim, Dell, & Schwartz, 2010). However, the higher learning rate for lexical learning in the present simulation could help to support fast learning of arbitrary associations and this is one of the features of declarative memory. Thus, while this simulation has similar assumptions to Ullman's account, the model does not fully implement the declarative components of his account.

The learning rate changes in the structure learning system created a clear sensitive period effect, where later AoA models performed noticeably worse than early AoA models. However, the later AoA models were still able to use the lexical learning system to support their English grammatical knowledge and their accuracy levels approached 65% (Fig. 7 A).

Analysis of rule learning revealed that there was a significant negative effect of AoA (Fig. 7 B, β = −0.02, SE = 0.001, χ ²(1) = 35.8, p < .001), a positive effect of LoE (β = 0.004, SE = 0.001, χ ²(1) = 131, p < .001) and a marginal negative effect of rule CP (β = −0.09, SE = 0.01, χ ²(1) = 3.1, p = .08). There were also three negative interactions between AoA and LoE (β = −0.0006, SE = 0.0001, χ ²(1) = 4.87, p = .03), AoA and rule CP (β = −0.02, SE = 0.001, χ ²(1) = 71.1, p < .001), and LoE and rule CP (β = −0.007, SE = 0.001, χ ²(1) = 23.5, p < .001). Finally, there was a three‐way interaction between AoA, LoE, and rule CP (Fig. 7 C), showing that with increasing AoA, higher CP rules benefited from increasing LoE less than lower CP rules (β = −0.001, SE = 0.0001, χ ²(1) = 37.9, p < .001).

Separating lexical and syntactic learning parts of the system successfully captures the effects observed in the Flege et al. data. Importantly, it showed that the LoE effect was weaker in later AoA models (Fig. 5 C). Also, the direction of the rule CP effect flipped from positive to negative. While the main effect of CP was marginal, its interaction with AoA and especially the three‐way interaction between AoA, LoE, and rule CP matched the human results showing that with increasing AoA, higher CP rules benefitted from increasing LoE less than lower CP rules.

After 16 years, the model's syntactic learning rate goes to zero and therefore the late learning models are learning to predict English words using Korean syntactic knowledge. Fig. 7 A shows that 19-22 learners do acquire the ability to correctly predict English words with an accuracy of around 70%. This relates to ERP evidence showing that late L2 learners exhibit similar syntactic P600 effects as native L1 speakers in some conditions (Foucart & Frenck‐Mestre, 2011; Sabourin et al., 2006). These effects are sometimes used to argue against critical period effects, since late learners are exhibiting similar patterns to native speakers. However, even though the late learning models do not have native‐like L2 syntactic representations, their L1 representations are sufficient to create differences across L2 rules. This is especially the case when behavior across the whole network/brain is averaged into a single measure like Rule Proportion/ERPs, where it can appear as if human/model learners are processing L2 sentences in a native‐like manner.

In this and the previous simulations, the models stopped receiving Korean language input once English was introduced as an L2. Although the complete suspension of L1 input is rare, there are many L2‐dominant bilinguals (Flege, Mackay, & Piske, 2002), particularly those with early AoA with long LoE in strongly monolingual environments who would be well characterized by this model. Furthermore, there are two populations which are similar to these models in that they show AoA effects even though they mainly receive input from one language: international adoptees and deaf learners of sign language. International adoptees are adopted into a new culture and exclusively get input from one language. Several studies have found that, while these learners have similar motivation and input to native learners, they acquire the language to a lower level than the equivalent native learners and language proficiency is negatively related to age of adoption (Gardell, 1979; Gauthier & Genesee, 2011; Hyltenstam et al., 2009). Deaf learners of sign languages also show AoA effects, even though sign language is their L1 and they are highly motivated (Boudreault & Mayberry, 2006; Mayberry, 2010; Mayberry & Eichen, 1991). These AoA effects support DeKeyser and Larson‐Hall ( 2009, p. 88) claim that "AoA keeps playing a large role when social and environmental variables are removed" and this suggests that some biological changes in learning ability may be involved in creating the sensitive period. Although the sensitive period is evident even when learning a single language, it is the case that most L2 learners continue to use the L1 after they start to receive L2 input and we examine whether this has an effect in simulation 4.

4.4.4. Simulation 4: Korean and English input in L2 learning

Our final simulation examines whether the results of the previous analyses generalize to an environment where the models receive both English and Korean input. Initially, the model learned Korean as an L1 and then it was given half‐English and half‐Korean input interleaved in a random order (akin to balanced bilinguals). To signal the target language, an additional language feature was added to the event semantics, which told the model which language it was producing. The syntactic and lexical learning rate parameters as well as other aspects of the simulation were identical to Simulation 3.

As in Simulation 3, late learning models did not achieve native‐like language accuracy (Fig. 8 A). There was a negative main effect of AoA effect (Fig. 8 B, β = −0.02, SE = 0.007, χ ²(1) = 27.5, p < .001), a positive effect of LoE (β = 0.01, SE = 0.0004, χ ²(1) = 137.6, p < .001), and a negative effect of rule CP (β = −0.08, SE = 0.007, χ ²(1) = 45.8, p < .001). There was a negative interaction between AoA by LoE (β = −0.001, SE = 0.0001, χ ²(1) = 37.3, p < .001), and a negative interaction between AoA and CP (β = −0.02, SE = 0.001, χ ²(1) = 28.6, p < .001). There was also a marginal interaction between LoE by rule CP (β = −0.002, SE = 0.001, χ ²(1) = 3.24, p = .007). Finally, there was a three‐way interaction between AoA, LoE, and rule CP (Fig. 8 C, β = −0.001, SE = 0.0001, χ ²(1) = 82.8, p < .001).

To better understand how bilingual input affected learning, we also examined the model's code‐switching behavior (e.g., producing Korean words in English sentences) in both simulations. Fig. 9 shows the proportion of Korean words produced by the models that received English‐only L2 training (Simulation 3) or English and Korean L2 training (Simulation 4). Late AoA models in Simulation 4 continued using many Korean words in English sentences even after a substantial number of years of English input. These results approximate the results of studies which have found that code‐switching rate was higher (14%) in late learners than in early learners (6%; Sheng, Bedore, Peña, & Fiestas, 2013). Code‐switching is very context dependent and this model does not fully capture all the factors that influence code‐switching. For example, Moore ( 2013) found that English‐learning Japanese speakers often switched to their L1 while preparing for an English presentation and the percentage of L1 could vary greatly within the same speaker depending on the proficiency of the interlocutor. Although AoA information was not provided for the learners in this study, there were some participants who used their L1 approximately 88% of the time, which approximates the high levels in late learners in Simulation 4.

In contrast to the marginal effect of CP in Simulation 3, the bilingual input in this simulation created a significant negative effect of CP. This means that even though the input for DET/PL was higher in the model's input, the model learned these rules less well compared to less frequent rules like 3PS/PST. We will discuss the source of these effects in the discussion. Overall, this model provided a good match to the effects of AoA, LoE, and CP seen in the Flege et al.'s reanalysis. In addition, it provided some evidence for code‐switching behavior within a model of sentence production that has learned both L1 and L2.

5. General discussion

This study of L2 learning examined the interaction between AoA and input factors like LoE and CP. In support of a critical/sensitive period, our reanalysis of Flege et al.'s ( 1999) data found a significant effect of AoA on L2 linguistic behaviors. Some studies have argued that entrenchment with connectionist activation functions can explain sensitive period effects (A. W. Ellis & Lambon Ralph, 2000; Munakata & McClelland, 2003). Simulation 1 examined this and found that these mechanisms alone were not sufficient to explain all the features of the sensitive period in the learning of grammatical knowledge. To simulate the sensitive period effects seen in humans, we changed the model's learning rates following a stretched Z function (Granena & Long, 2013). Our claim is that this learning rate is an age‐dependent learning parameter that influences L1 and L2 learning equally (some L1 phenomena can also be explained with learning rate changes, e.g., Peter, Chang, Pine, Blything, & Rowland, 2015). We can contrast this with the view that the critical period reflects specialized linguistic parameters, such as a head‐direction parameter (e.g., Chomsky & Lasnik, 1993), which are set within the critical period. Instead, the use of general learning parameters here suggests that linguistic critical periods could be due to mechanisms that evolved originally for non‐linguistic critical period phenomena (Knudsen, 2004; chick imprinting; Lorenz, 1937; birdsong; Marler, 1970; cochlear implants; Harrison, Gordon, & Mount, 2005).

The learning rate changes in the model may also have a role in social/motivational/input‐based accounts of the sensitive period. For example, it could be the case that children receive more optimal input for language learning than adults. In order for this input to create sensitive period effects, the knowledge that is learned from early optimal input should not be overwritten by the sometimes more than 20 years of less optimal adult input. The model's stretched Z learning function is one way to ensure that early experiences due to various factors persist in spite of further learning. Thus, regardless if one believes in a purely biological account of the sensitive period, or in a social/motivational/input‐based account, there needs to be an age‐dependent learning mechanism that insures that this early experience persists such that it can influence testing that takes place years later.

The main impetus for the present work was the finding that the amount of L2 input was a poor predictor of proficiency (DeKeyser, 2000; DeKeyser et al., 2010; Johnson & Newport, 1989; Lee & Schacter, 1997; McDonald, 2000). Such findings are compounded by evidence suggesting that some L2 learners are better at recognizing the grammatical use of lower frequency rules like the third person singular than higher frequency rules like determiners (Flege et al., 1999; Johnson & Newport, 1989). To explain this, we used corpus analyses to characterize the frequency of different rules (rule CP) and used this to factor out rule variation. When rule CP was added to the Flege et al.'s reanalysis, LoE went from non‐significant to a significant positive effect, which suggests that the lack of LoE effects in some studies may be due to the fact that this effect was obscured by rule variation. LoE was also significant when rule was included as a factor, which demonstrates that this result does not depend on a particular approach to computing rule CPs.

We also found that late AoA learners were less sensitive to the input (LoE) than early AoA learners. Our simulation 2 showed that the stretched Z learning function was not sufficient to explain this interaction. To model this effect in simulation 3, we assigned separate learning rates to the lexical and syntactic parts of the system (Paradis, 2004; Ullman, 2001). The lexical part retained a high learning rate throughout the training, whereas the syntactic learning rate followed the stretched Z function. The early AoA models had a high syntactic learning rate, which allowed them to reconfigure their Korean syntactic representations into representations that were more appropriate for English. However, the later AoA models had a low syntactic learning rate and hence their high lexical learning rate forced them to associate English words with sequence representations that were still partially Korean. On this account, the weaker effect of LoE in late AoA learners is due loss of syntactic learning ability in the late learners and their greater dependence on lexical learning as a result. This account is supported by ERP studies of L2 learners' brain activity that have found that syntactic components such as the P600 differ from native learners more than lexical‐semantic components such as the N400 (e.g., Hahne, 2001; Hahne & Friederici, 2001; Weber‐Fox & Neville, 1996). Furthermore, recent studies have tested grammatical distinctions that yield P600 effects in native speakers and proficient L2 learners, but which yield N400 effects in some late AoA L2 learners (McLaughlin et al., 2010). Since the N400 is traditionally associated with lexical/semantic expectations, N400 effect for a grammatical distinction supports the claim that late AoA learners may be using lexical learning to a greater degree than early AoA learners to support their syntactic processing in the L2.

Although the syntactic learning rate in the model was completely switched off at age 16, this did not fully impair the model's ability to learn syntactic regularities and to differentiate between different rules. This is because the lexical and syntactic learning rates are both being used to learn word regularities that support syntactic grammaticality judgments (e.g., DET rule depends on predicting the word the after verbs). This means that lexical and syntactic behaviors may not be transparently related to lexical and syntactic learning in human and model behavior (see the syntactic/lexical division of labor in Chang, 2002; Gordon & Dell, 2003). For example, Granena and Long ( 2013) argued that lexical learning ability follows a similar negative learning function as syntactic learning, but their measure of lexical learning involves multi‐word collocations, which in our model would be encoded in the sequencing system and would be sensitive to the syntactic learning rate. We have shown here that lexical learning can be used to learn grammaticality constraints in a way that mimics the behavior in late L2 learners. Overall, our account predicts that under similar input conditions, early AoA learners can use their higher syntactic learning rate to learn deeper and more abstract syntactic rules than later AoA learners and support for this can be found in Hudson Kam and Newport ( 2005) study, which found that children were more likely than adults to regularize the artificial language that they were taught.

Although input is important for L2 learning, some L2 learners appear to perform worse with higher frequency rules like determiners than lower frequency rules like third‐person singular. There was a significant negative effect of rule CP in our reanalysis of Flege et al. ( 1999) study and similar effects have been found in other studies (DeKeyser, 2000; Johnson, 1992; McDonald, 2000; Murakami & Alexopoulou, 2016). Since the effect is negative, it is not straightforwardly explained by input‐based theories (N. C. Ellis, 2002). A likely explanation is transfer/interference from the L1, but it is often hard to formalize the morphosyntactic similarity across languages. The fact that the model does not capture this negative relationship in Simulation 1 and 2 suggests that the separate learning rates for lexical and syntactic knowledge in Simulation 3 and 4 are important in capturing these effects. Low‐frequency rules like PST and 3PS were relatively simple and the late learning models were able to correctly predict English structures with Korean syntactic representation using lexical learning to linking English words with these representations. The higher frequency DET/PL/PAR rules were more complex and harder to predict from Korean representations (these rules depend more on learned syntactic knowledge). What the model highlights is an implicit assumption of transfer accounts, which is that transfer from the L1 assumes that the L2 syntax is learned slowly enough to make it preferable to link L2 words to L1 structures and this assumption is instantiated by a gradual reduction in the syntactic learning rate, whereas lexical learning rate remained high. Although we do not know the exact nature of the L1/L2 similarity that determines transfer/interference between languages, the model provides an explicit implementation of a mechanism that captures some of these transfer effects and future work should examine the nature of this mechanism and its relation to equivalent transfer effects in human studies.

The models presented here are not fully realistic simulations of L2 learners. Rather, like the mixed model reanalysis, they provided a simplified representation of a complex pattern of data. It is also not the case that one simulation is the best simulation of all L2 speakers. It may be the case that early AoA learners and learners with greater LoE are more likely to be exposed to exclusively L2 input as in Simulation 3 (L2‐dominant bilinguals; Flege et al., 2002), whereas late AoA learners and learners who have only a short LoE are more likely to maintain connections to their L1 as in Simulation 4 (balanced bilinguals). Furthermore, different results would arise if the same model was trained on different L1/L2 pairs (Murakami & Alexopoulou, 2016) and the present simulations do not explain variation in implicit and explicit aspects of L2 tasks (R. Ellis, 2005; Chang et al., 2012). The main purpose of these models is to offer a starting point for developing a computational account of L2 learning.

The main innovation in the present work is the demonstration that a model of L1 language acquisition and production can explain L2 performance over various AoA, LoE, and grammatical rules. The extension to L2 learning involved minor changes in learning rates without any major architectural changes. Since the same network/mechanism is used for encoding L1 and L2 rules, the model predicts that there will be transfer between L1 and L2 structures (Foucart & Frenck‐Mestre, 2012; Hartsuiker et al., 2004; Ionin & Montrul, 2010; MacWhinney, 2005; Sabourin et al., 2006) and similar brain areas/ERP signatures for L1 and L2 processing (Friederici, Steinhauer, & Pfeifer, 2002; Kotz, 2009). Learning rate variation in syntactic and lexical systems offers an account which allows the same learning mechanism and network to explain the large differences due to AoA. Overall, this approach provides an explicit account of the complex interactions of various aspects of L1 and L2 structure learning.

Acknowledgments

This research was funded by an ESRC grant (number ES⁄J500094⁄1). Marius Janciauskas and Franklin Chang are members of the International Centre for Language and Communicative Development (LuCiD) at the University of Liverpool. The support of the Economic and Social Research Council [ES/L008955/1] is gratefully acknowledged. We thank James Flege for sharing the Flege et al. ( 1999) data with us.

Appendix A.

A.1.

The simulations were implemented using Version OSX‐1.0a of the LENS connectionist software package (Brouwer, de Kok, & Fitz, 2013; Rohde, 1999). Unless stated otherwise, default parameters of the simulator were used. Simulation is available at https://sites.google.com/site/sentenceproductionmodel/Home/l2model.

The model had 109 units in the Previous Word/Produced Word layers, 20 units in the Compress/CCompress layers, 30 units in the Hidden/Context layers, 5 units in the Role/CRole/CRole Copy layers, 43 units in the Concept/CConcept layers, and 6 units in the Event Semantics layer. A Context layer held a copy of the activation of the Hidden layer and was reset to 0.5 at the start of an utterance. The Previous Word layer received one‐to‐one inputs from all the Produced Word layer units and from the previous target inputs, and a winner‐take‐all filter was applied. Thus, during learning from the speech of others, the Previous Word was set to the heard target word. However, when the model was generating its own utterance, the Previous Word layer was set to the word that model had previously produced.

The CRole layer had soft‐max activation function. The CRole Copy layer (Fig. 2 , top panel, center) which averaged a copy of its own previous activation with the activation of the CRole layer. To help the model learn the links between the previous word and its appropriate concept (i.e., the weights between the Previous Word and CConcept layers, Fig. 2 , far left), the previous activation of the Concept layer was used as a training signal for the CConcept layer. The Role‐Concept links in the production message, the CConcept‐CRole links in the comprehension message, and Event Semantics activations were all set before a training or test sentence was processed.

Unless specified otherwise, units in all layers used the sigmoidal logistic activation function, with activation values running between 0 and 1. The Produced Word layer used the logistic activation function in simulation 1 and soft‐max in the other simulations. Weights were initially set to the values uniformly sampled between −0.5 and 0.5. Units were unbiased, unless specifically mentioned, to make the layers more dependent on their inputs for their behavior. However, Concept units were biased to −3 to ensure that they had a low default activation level.

A version of back‐propagation was used to train the model where derivatives were clipped at 1.0 (Doug's momentum; Rohde, 1999). Momentum was 0.9. Weights were updated after each message‐sentence pair had been trained. Training began by randomizing all weights (same model seed was used for all runs, but input generation used different seeds). At the start of each utterance, the message was set. After the sentence was generated, the sequence of Produced Word activations was processed by a decoder program that yielded the produced sentence. Sentences were then processed by a syntactic coder program that added the syntactic and message tags. The model's output was compared with the target sentence and the sentence was considered accurate if the all the words were correctly produced.

References

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Mizumoto, T. , Hayashibe, Y. , Komachi, M. , Nagata, M. , & Matsumoto, Y. (2012). The effect of learner corpus size in grammatical error correction of ESL writings. In 24th International Conference on Computational Linguistics (p. 863-872).

Nakagawa, S. , & Schielzeth, H. (2013). A general and simple method for obtaining [R ² from generalized linear mixed‐effects models. Methods in Ecology and Evolution, 4(2), 133-142. Google Scholar]

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Input and Age‐Dependent Variation in Second Language Learning: A Connectionist Account

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Language learning requires linguistic input, but several studies have found that knowledge of second language (L2) rules does not seem to improve with more language exposure (e.g., Johnson & Newport, 1989). One reason for this is that previous ...

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Association for Psychological Science - APS

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How do children learn language, and how is language related to other cognitive and social skills? For decades, the specialized field of developmental psycholinguistics has studied how children acquire language-or multiple languages-taking into account biological, neurological, and social factors that influence linguistic developments and, in turn, can play a role in how children learn and socialize. Here's a look at recent research (2020-2021) on language development published in Psychological Science.
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Children can acquire language rapidly, possibly because their caregivers use language in ways that support such development. Specifically, caregivers' language is often fine-tuned to children's current linguistic knowledge and vocabulary, providing an optimal level of complexity to support language learning. In their new research, Leung and colleagues add to the body of knowledge involving how caregivers foster children's language acquisition.

The researchers asked individual parents to play a game with their child (age 2-2.5 years) in which they guided their child to select a target animal from a set. Without prompting, the parents provided more informative references for animals they thought their children did not know. For example, if a parent thought their child did not know the word "leopard," they might use adjectives ("the spotted, yellow leopard") or comparisons ("the one like a cat"). This indicates that parents adjust their references to account for their children's language knowledge and vocabulary-not in a simplifying way but in a way that could increase the children's vocabulary. Parents also appeared to learn about their children's knowledge throughout the game and to adjust their references accordingly.
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This research tracked real-time brain activation during infant-adult interactions, providing an innovative measure of social interaction at an early age. When communicating with infants, adults appear to be sensitive to subtle cues that can modify their brain responses and behaviors to improve alignment with, and maximize information transfer to, the infants.

Piazza and colleagues used functional near-infrared spectroscopy-a noninvasive measure of blood oxygenation resulting from neural activity that is minimally affected by movements and thus allows participants to freely interact and move-to measure the brain activation of infants (9-15 months old) and adults while they communicated and played with each other. An adult experimenter either engaged directly with an infant by playing with toys, singing nursery rhymes, and reading a story or performed those same tasks while turned away from the child and toward another adult in the room.

Results indicated that when the adult interacted with the child (but not with the other adult), the activations of many prefrontal cortex (PFC) channels and some parietal channels were intercorrelated, indicating neural coupling of the adult's and child's brains. Both infant and adult PFC activation preceded moments of mutual gaze and increased before the infant smiled, with the infant's PFC response preceding the adult's. Infant PFC activity also preceded an increase in the pitch variability of the adult's speech, although no changes occurred in the adult's PFC, indicating that the adult's speech influenced the infant but probably did not influence neural coupling between the child and the adult.
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Language and communication are important for social and cognitive development. Although deaf and hard-of-hearing (DHH) children born to deaf parents can communicate with their caregivers using sign language, most DHH children are born to hearing parents who do not have experience with sign language. These children may have difficulty with early communication and experience developmental delays. For instance, the development of theory of mind-the understanding of others' mental states-is usually delayed in DHH children born to hearing parents.

Yu and colleagues studied how providing DHH children with hearing devices early in life (before 2 years of age) might enrich their early communication experiences and benefit their language development, supporting the typical development of other capabilities-in particular, theory of mind. The researchers show that 3- to 6-year-old DHH children who began using cochlear implants or hearing aids earlier had more advanced language abilities, leading to better theory-of-mind growth, than children who started using hearing provisions later. These findings highlight the relationships among hearing, language, and theory of mind.
Cassandra J. Lowe, Isu Cho, Samantha F. Goldsmith, and J. Bruce Morton (2021)
Acommon idea is that bilingual children, who grow up speaking two languages fluently, perform better than monolingual children in diverse executive-functioning domains (e.g., attention, working memory, decision making). This meta-analysis calls that idea into question.

Lowe and colleagues synthesized data from studies that compared the performance of monolingual and bilingual participants between the ages of 3 and 17 years in executive-functioning domains (1,194 effect sizes). They found only a small effect of bilingualism on participants' executive functioning, which was largely explained by factors such as publication bias. After accounting for these factors, bilingualism had no distinguishable effect. The results of this large meta-analysis thus suggest that bilingual and monolingual children tend to perform at the same level in executive-functioning tasks. Bilingualism does not appear to boost performance in executive functions that serve learning, thinking, reasoning, or problem solving.

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The Littlest Linguists: New Research on Language Development

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How do children learn language, and how is language related to other cognitive and social skills? For decades, the specialized field of developmental psycholinguistics has studied how children acquire language-or multiple languages-taking into account biological, neurological, and social factors that influence linguistic developments and, in turn, can play a role in how children learn and socialize.

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Basic Phonogram Chart by Logic of English

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Paquete de Libros Decodificables | Decodable books in Spanish by TeachAlicious

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What you'll learn

How sensory perception works in the brain

How the physiology of vision, hearing, taste, smell, touch, motor control and other senses work

The basic anatomy of functional areas of the brain

The visual system of the brain

How the motor subsystems of the brain execute and coordinate our movement.

The critical brain systems that keep alive

Course description

The human brain is a fantastically complex system, capable of transforming a torrent of incoming information into thought and action. In this course, we will look at how the various subsystems of the brain work together to enable us to survive and thrive in a changing world.

Each lesson will challenge you with interactive segments, animations, and documentaries that explore the richness and complexity of the brain. Our forums will provide you with a place to meet other students around the world, and you can learn from each other through a series of discussion questions.

Do you want to learn about how brains perceive the world? Join us as we explore sensation, perception and the physiology of functional regions of the brain.

Course Outline

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Fundamentals of Neuroscience, Part 3: The Brain | Harvard University

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Discover what makes your brain tick in this third course in our introductory series in neuroscience.

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A few weeks back, I shared ideas on ELA stations for primary students after this was brought up during a coaching cycle with one of my schools. Upon reflection, I decided to do the same with math.

Creating math stations for primary students can be a fun and effective way to introduce them to basic math concepts through hands-on activities. These stations can help develop their understanding of numbers, shapes, patterns, and measurements in an interactive and engaging manner.

Here are some ideas for math stations suitable for students in PreK - 1st grade:

Counting: Practice counting and number recognition by having students use items like beads, blocks, or buttons for children to count. Include number cards so they match the quantity to the correct number.

Shape Sorting: Provide a variety of geometric shapes in different colors and sizes to identify and sort. Have children sort them by shape, size, or color into designated bins or onto labeled mats.

Pattern Making: Recognize and create patterns where students use colored beads, blocks, or stickers to create simple patterns. Encourage children to continue the patterns or create their own.

Measurement: Explore concepts of size and measurement by offering various items to measure with non-standard units, such as paper clips, blocks, or hands. Include activities like comparing the height of plants or the length of objects.

Simple Addition and Subtraction: Introduce essential addition and subtraction using visual aids like number lines, counting bears, or finger puppets to help children understand adding and taking away.

Number Writing: Practice writing numbers by providing dotted numbers for tracing, blank paper for free writing, and activities that involve writing numbers related to counting objects.

Math Storybooks: Combine literacy with math by selecting storybooks incorporating math concepts (e.g., counting, shapes). After reading, children can engage in related math activities or crafts.

Geometry Block Building: Understand spatial relationships and geometry by offering blocks of different shapes and sizes for accessible building. Challenge students with specific building tasks that require using certain shapes.

Graphing: Introduce data collection and representation by using simple graphing activities, such as graphing favorite fruits or the color of socks children are wearing. Provide stickers or stamps for children to fill in their answers on a chart.

Time Telling: Familiarize students with time using teaching clocks to show different times. Include puzzles that match times to daily activities (e.g., lunchtime, nap time).

Technology: This is a great way to integrate tech purposefully and acquire data using adaptive tools when appropriate. Use tablets or computers with ed-tech tools focused on math concepts, such as counting, simple addition/subtraction, measurement, shape sorting/identification, and patterns.

Each station can be adjusted to match the children's developmental level. It should be supervised to ensure that students grasp the concepts and to provide help as needed. Rotating the stations every few weeks can keep the activities fresh and engaging for the students. As with all station tasks, ensure that a sound pedagogical design is in place and that there is a targeted instruction or support rotation.

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A blog about digital leadership, pedagogy, learning, and transformative change in education.

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PRACTICING NEUROINCLUSIVE LISTENING | NE Academy

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The bedrock of any communication consists of patience, understanding, and empathy. Given that all of us are unique in how we process information and engage with each other, what works for one individual when it comes to communication may not work for another.

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Researchers are continually conducting studies to learn more about the causes of dyslexia, early identification of dyslexia, and the most effective treatments for dyslexia.
Developmental dyslexia is associated with difficulty in processing the orthography (the written form) and phonology (the sound structure) of language. As a way to understand the origin of these problems, neuroimaging studies have examined brain anatomy and function of people with and without dyslexia. These studies are also contributing to our understanding of the role of the brain in dyslexia, which can provide useful information for developing successful reading interventions and pinpointing certain genes that may also be involved.

What is brain imaging?

A number of techniques are available to visualize brain anatomy and function. A commonly used tool is magnetic resonance imaging (MRI), which creates images that can reveal information about brain anatomy (e.g., the amount of gray and white matter, the integrity of white matter), brain metabolites (chemicals used in the brain for communication between brain cells), and brain function (where large pools of neurons are active). Functional MRI (fMRI) is based on the physiological principle that activity in the brain (where neurons are "firing") is associated with an increase of blood flow to that specific part of the brain. The MRI signal bears indirect information about increases in blood flow. From this signal, researchers infer the location and amount of activity that is associated with a task, such as reading single words, that the research participants are performing in the scanner. Data from these studies are typically collected on groups of people rather than individuals for research purposes only-not to diagnose individuals with dyslexia.

Which brain areas are involved in reading?

Since reading is a cultural invention that arose after the evolution of modern humans, no single location within the brain serves as a reading center. Instead, brain regions that sub serve other functions, such as spoken language and object recognition, are redirected (rather than innately specified) for the purpose of reading (Dehaene & Cohen, 2007). Reading involves multiple cognitive processes, two of which have been of particular interest to researchers: 1) grapheme-phoneme mapping in which combinations of letters (graphemes) are mapped onto their corresponding sounds (phonemes) and the words are thus "decoded," and 2) visual word form recognition for mapping of familiar words onto their mental representations. Together, these processes allow us to pronounce words and gain access to meaning. In accordance with these cognitive processes, studies in adults and children have demonstrated that reading is supported by a network of regions in the left hemisphere (Price, 2012), including the occipito-temporal, temporo-parietal, and inferior frontal cortices. The occipito-temporal cortex holds the "visual word form area." Both the temporo-parietal and inferior frontal cortices play a role in phonological and semantic processing of words, with inferior frontal cortex also involved in the formation of speech sounds. These areas have been shown to change as we age (Turkeltaub, et al., 2003) and are altered in people with dyslexia (Richlan et al., 2011).

What have brain images revealed about brain structure in dyslexia?

Evidence of a connection between dyslexia and the structure of the brain was first discovered by examining the anatomy of brains of deceased adults who had dyslexia during their lifetimes. The left-greater-than-right asymmetry typically seen in the left hemisphere temporal lobe (planum temporale) was not found in these brains (Galaburda & Kemper, 1979), and ectopias (a displacement of brain tissue to the surface of the brain) were noted (Galaburda, et al., 1985). Then investigators began to use MRI to search for structural images in the brains of research volunteers with and without dyslexia. Current imaging techniques have revealed less gray and white matter volume and altered white matter integrity in left hemisphere occipito-temporal and temporo-parietal areas. Researchers are still investigating how these findings are influenced by a person's language and writing systems.

What have brain images revealed about brain function in dyslexia?

Early functional studies were limited to adults because they employed invasive techniques that require radioactive materials. The field of human brain mapping greatly benefited from the invention of fMRI. fMRI does not require the use of radioactive tracers, so it is safe for children and adults and can be used repeatedly which facilitates longitudinal studies of development and intervention. First used to study dyslexia in 1996 (Eden et al., 1996), fMRI has since been widely used to study the brain's role in reading and its components (phonology, orthography, and semantics). Studies from different countries have converged in findings of altered left-hemisphere areas (Richlan et al., 2011), including ventral occipito-temporal, temporo-parietal, and inferior frontal cortices (and their connections). Results of these studies confirm the universality of dyslexia across different world languages.

What about genes, brain chemistry, and brain function?

Several genetic variants are associated with dyslexia, and their impact on the brain has been investigated in people and mice. Using animals that have been bred to have genes associated with dyslexia, researchers are investigating how these genes might affect development of and communication among brain regions (Che, et. al., 2014; Galaburda, et al., 2006). These investigations dove-tail with studies in humans. Differences in brain anatomy (Darki, et al., 2012; Meda et al., 2008) and brain function (Cope et al., 2012; Pinel et al., 2012) have been observed in people who carry dyslexia-associated genes, even those people who have good reading skills. In addition to these investigations at the anatomical, physiological, and molecular levels, researchers are trying to pinpoint the chemical connection to dyslexia. For example, brain metabolites that play a role in allowing neurons to communicate can be visualized using another MRI-based technique called spectroscopy. Several metabolites (for example, choline) are thought to be different in people with dyslexia (Pugh et al., 2014). Researchers continue to explore the connections between these findings and are hopeful that what they learn will help to determine the causes of dyslexia. This is a difficult aspect of research because differences in the brains of people with dyslexia are not necessarily the cause of their reading difficulties (for example, it could also be a consequence of reading less).

Changes in Reading, Changes in the Brain

Brain imaging research has revealed anatomical and functional changes in typically developing readers as they learn to read (e.g. Turkeltaub et al., 2003), and in children and adults with dyslexia following effective reading instruction (Krafnick, et al., 2011; Eden et al., 2004). Such studies also shed light onto the brain-based differences of those children with dyslexia who benefit from reading instruction compared to those who fail to make gains (Davis et al., 2011; Odegard, et al., 2008). Neuroimaging data have also been used to predict long-term reading success for children with and without dyslexia (Hoeft et al., 2011).

Cause versus Consequence

An important aspect of research on the brain and reading is to determine whether the findings are the cause or the consequence of dyslexia. Some of the brain regions known to be involved in dyslexia are also altered by learning to read, as demonstrated by comparisons of adults who were illiterate but then learned to read (Carreiras et al., 2009). Longitudinal studies in typical readers reveal anatomical changes with age, some of which are related to development (Giedd et al., 1999) and others to the firming up of language skills (Sowell et al., 2004) in correlation with improvements in phonological skills (Lu et al., 2007). As such, researchers are teasing apart the brain-based differences that can be observed before children begin to learn to read from differences that may occur as a consequence of less reading by people with dyslexia. For example, researchers have found altered brain anatomy (Raschle, et al., 2011) and function (Raschle, et al., 2012) in pre-reading children with a family history of dyslexia. Future studies using longitudinal designs (i.e., long term), will inform the timeline of these changes and clarify cause and consequences of anatomical and functional differences in dyslexia.

Summary

The role of the brain in developmental dyslexia has been studied in the context of brain anatomy, brain chemistry, and brain function-and in combination with interventions to improve reading and information about genetic influences. Together with results of behavioral studies, this information will help researchers to identify the causes of dyslexia, continue to explore early identification of dyslexia, and determine the best avenues for its treatment.

References

Carreiras, M., Seghier, M. L., Baquero, S., Estévez, A., Lozano, A., Devlin, J. T., & Price, C. J. (2009). An anatomical signature for literacy. Nature, 461(7266), 983-986. doi:10.1038/nature08461

Che, A., Girgenti, M. J., & Loturco, J. (2014). The Dyslexia-Associated Gene Dcdc2 is required for spike-timing precision in mouse neocortex. Biological Psychiatry, in press. doi:10.1016/j.biopsych.2013.08.018

Cope, N., Eicher, J. D., Meng, H., Gibson, C. J., Hager, K., Lacadie, C., ... Gruen, J. R. (2012). Variants in the DYX2 locus are associated with altered brain activation in reading-related brain regions in subjects with reading disability. NeuroImage, 63(1), 148-156. doi:10.1016/j.neuroimage.2012.06.037

Darki, F., Peyrard-Janvid, M., Matsson, H., Kere, J., & Klingberg, T. (2012). Three Dyslexia Susceptibility Genes, DYX1C1, DCDC2, and KIAA0319, affect temporo-parietal white matter structure. Biological Psychiatry, 72(8), 671-676. doi:10.1016/j.biopsych.2012.05.008

Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56(2), 384-398. doi:10.1016/j.neuron.2007.10.004

Eden, G. F., Jones, K. M., Cappell, K., Gareau, L., Wood, F. B., Zeffiro, T. A., ... Flowers, D. L. (2004). Neural changes following remediation in adult developmental dyslexia. Neuron, 44(3), 411-422.

Eden, G. F., VanMeter, J. W., Rumsey, J. M., Maisog, J. M., Woods, R. P., & Zeffiro, T. A. (1996). Abnormal processing of visual motion in dyslexia revealed by functional brain imaging. Nature, 382(6586), 66-69. doi:10.1038/382066a0

Flowers, D. L., Wood, F. B., & Naylor, C. E. (1991). Regional cerebral blood flow correlates of language processes in reading disability. Archives of Neurology, 48(6), 637-643.

Galaburda, A. M., & Kemper, T. L. (1979). Cytoarchitectonic abnormalities in developmental dyslexia: a case study. Annals of Neurology, 6(2), 94-100. doi:10.1002/ana.410060203

Galaburda, A. M., Sherman, G. F., Rosen, G. D., Aboitiz, F., & Geschwind, N. (1985). Developmental dyslexia: four consecutive patients with cortical anomalies. Annals of Neurology, 18(2), 222-233. doi:10.1002/ana.410180210

Galaburda, A. M., LoTurco, J., Ramus, F., Fitch, R. H., & Rosen, G. D. (2006). From genes to behavior in developmental dyslexia. Nature Neuroscience, 9(10), 1213-1217. doi:10.1038/nn1772

Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., ... Rapoport, J. L. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2(10), 861-863. doi:10.1038/13158

Gross-Glenn, K., Duara, R., Barker, W. W., Loewenstein, D., Chang, J. Y., Yoshii, F., ... Sevush, S. (1991). Positron emission tomographic studies during serial word-reading by normal and dyslexic adults. Journal of Clinical and Experimental Neuropsychology, 13(4), 531-544. doi:10.1080/01688639108401069

Hoeft, F., McCandliss, B. D., Black, J. M., Gantman, A., Zakerani, N., Hulme, C., ... Gabrieli, J. D. E. (2011). Neural systems predicting long-term outcome in dyslexia. PNAS, 108(1), 361-6. doi:10.1073/pnas.1008950108

Krafnick, A. J., Flowers, D. L., Napoliello, E. M., & Eden, G. F. (2011). Gray matter volume changes following reading intervention in dyslexic children. NeuroImage, 57(3), 733-741. doi:10.1016/j.neuroimage.2010.10.062

Lu, L. H., Leonard, C. M., Thompson, P. M., Kan, E., Jolley, J., Welcome, S. E., ... Sowell, E. R. (2007). Normal developmental changes in inferior frontal gray matter are associated with improvement in phonological processing: a longitudinal MRI analysis. Cerebral Cortex, 17(5), 1092-9. doi:10.1093/cercor/bhl019

Meda, S. A., Gelernter, J., Gruen, J. R., Calhoun, V. D., Meng, H., Cope, N. A., & Pearlson, G. D. (2008). Polymorphism of DCDC2 reveals differences in cortical morphology of healthy individuals-A preliminary voxel based morphometry study. Brain Imaging and Behavior, 2(1), 21-26. doi:10.1007/s11682-007-9012-1

Pinel, P., Fauchereau, F., Moreno, A., Barbot, A., Lathrop, M., Zelenika, D., ... Dehaene, S. (2012). Genetic variants of FOXP2 and KIAA0319/TTRAP/THEM2 locus are associated with altered brain activation in distinct language-related regions. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 32(3), 817-825. doi:10.1523/JNEUROSCI.5996-10.2012

Price, C. J. (2012). A review and synthesis of the first 20years of PET and fMRI studies of heard speech, spoken language and reading. Neuroimage, 62(2), 816-847.

Pugh, K. R., Frost, S. J., Rothman, D. L., Hoeft, F., Tufo, S. N. D., Mason, G. F., ... Fulbright, R. K. (2014). Glutamate and choline levels predict individual differences in reading ability in emergent readers. The Journal of Neuroscience, 34(11), 4082-4089. doi:10.1523/JNEUROSCI.3907-13.2014

Raschle, N. M., Chang, M., & Gaab, N. (2011). Structural brain alterations associated with dyslexia predate reading onset. Neuroimage, 57(3), 742-749.

Raschle, N. M., Zuk, J., & Gaab, N. (2012). Functional characteristics of developmental dyslexia in left-hemispheric posterior brain regions predate reading onset. Proceedings of the National Academy of Sciences of the United States of America, 109(6), 2156-2161. doi:10.1073/pnas.1107721109

Richlan, F., Kronbichler, M., & Wimmer, H. (2011). Meta-analyzing brain dysfunctions in dyslexic children and adults. Neuroimage, 56(3), 1735-1742. doi:10.1016/j.neuroimage.2011.02.040
p style="text-indent: -0.5in; margin-left: 0.5in;">Richlan, F., Kronbichler, M., & Wimmer, H. (2013). Structural abnormalities in the dyslexic brain: A meta-analysis of voxel-based morphometry studies. Human Brain Mapping, 34(11), 3055-3065. doi:10.1002/hbm.22127

Rumsey, J. M., Andreason, P., Zametkin, A. J., Aquino, T., King, A. C., Hamburger, S. D., ... Cohen, R. M. (1992). Failure to activate the left temporoparietal cortex in dyslexia. An oxygen 15 positron emission tomographic study. Archives of Neurology, 49, 527-534.

Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., & Toga, A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. The Journal of Neuroscience, 24(38), 8223-8231. doi:10.1523/JNEUROSCI.1798-04.2004

Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003). Development of neural mechanisms for reading. Nature Neuroscience, 6(7), 767-773. doi:10.1038/nn1065
The International Dyslexia Association (IDA) thanks Guinevere F. Eden, Ph.D., for her assistance in the preparation of this fact sheet. © Copyright The International Dyslexia Association (IDA). For copyright information, please click here.

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Researchers are continually conducting studies to learn more about the causes of dyslexia, early identification of dyslexia, and the most effective treatments for dyslexia. Developmental dyslexia is associated with difficulty in processing the orthography (the written form) and phonology (the sound structure) of language.

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Effect of prior knowledge on good and poor readers’ memory of text.

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We investigated how prior knowledge influences the amount of short-term nonverbal and verbal memory and long-term retention in students of high and low ability in reading comprehension. Sixty-four junior high students were divided into four equal-sized groups on the basis of preassessed reading ability (high and low) and preassessed amount of existing prior knowledge about baseball (high and low). Each subject silently read an account of a half inning of a baseball game. After reading, each subject recalled the account nonverbally by moving figures and verbally by retelling the story. After an interpolated task, they summarized the game and sorted passage sentences for idea importance. There was a significant main effect for prior knowledge on all measures. No interactions between prior knowledge and ability were found. These results delineate the powerful effect of prior knowledge. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

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We empower educators to reimagine and redesign learning through impactful pedagogy and meaningful technology use. We achieve this by offering transformative professional learning, fostering vibrant communities, and ensuring that digital tools and experiences are accessible and effective.

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The Secrets to Assistant Principal Success

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Baruti Kafele, school leadership expert and author of The Assistant Principal Identity, discusses the key attributes and inner qualities of effective APs-and how they can be developed and supported.

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What's the Research Behind Mouth Picture Sound Walls?

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All of us obviously want to help our students become better writers. But are there ways we can "double-dip," too-in other words, help them improve their writing AND also use writing instruction to improve reading skills?

We'll explore that question today with Tony Zani, Mary Tedrow, Mary Beth Nicklaus, Colleen Cruz, and Pam Allyn. You can listen to a 10-minute conversation I had with Tony, Mary, and Mary Beth on my BAM! Radio Show. You can also find a list of, and links to, previous shows here.

Giving kids the 'write stuff' makes them better readers
Tony Zani is a literacy coach in the Salt Lake City school district. He has a bachelor's degree in elementary education and a master's degree in instructional leadership. Tony is a national-board-certified teacher with a specialization in early-childhood education:
Writing is often the overlooked content area. After the National Reading Panel left it out and No Child Left Behind focused on reading achievement, there seemed to be a decline in teaching writing. After the Common Core State Standards came out, there was an increase in writing instruction. But, if your state is like mine, writing is only tested in a few grades. So, guess what? Those are the grades when writing is taught like crazy. In other grades, it often becomes a nice thing "if there's time." There's rarely time.

This mentality is prevalent because every level of the education system focuses on making sure students do well on end-of-year, high-stakes assessments. Jobs are at stake. Money from the government is at stake. Heaven forbid your school does so poorly that an outside group comes in to help you "turnaround."

Never fear, though. Writing directly benefits students' reading skills. For example, if you have students write about what they've read or learned (for nearly any content or age), you'll dramatically improve reading comprehension. Students are often forced to reread and think more deeply about what they've read. When students have to consider a controversial question and use texts they've read to defend their point of view, reading comprehension is off the charts. In our school, we've emphasized writing about what we read. It took about two years for most teachers, and students, to really embrace the concept. It was about that time that our end-of-year reading scores had a huge jump. Our highly impacted Title I school made enormous growth just because students were better at thinking about what they read.

Writing also improves students' reading fluency. When students have to stop and think about what spelling patterns to use when they write, they are making a deeper connection in their brains about sound and spelling patterns. This deeper connection makes it easier, and faster, for students to recall those same patterns when they read. Written language is literally a secret code that someone made up to represent spoken sounds. The more students think about and practice the code in written form, the better they will be at understanding the same code in writing. Again, in our high-needs school, we saw students' scores on tests like DIBELS and our end-of-level test rise dramatically. Fluent readers more deeply understand that code.

Writing also improves reading comprehension as students get better at formatting their writing. When students write argumentative essays, they learn how authors often lay out their arguments and evidence. This, in turn, gives students a framework for reading others' argumentative writing. Having a framework in your mind helps you fill in the blanks and improves comprehension. When students write narrative pieces, they develop an understanding of how authors typically lay out character development, setting, plot, problems, turning points, and resolutions. Again, students have a framework to build upon when they read others' narrative texts. In a bit of irony, our school focused on writing informative and argumentative pieces-those are emphasized in the common core, right? Our students had very high scores when reading informational texts. However, students scored lower when reading literature. Reading literature was a strength for most other schools. Writing in all genres is important. Don't lose that balance!

Writing is a critical communication skill. Universities and employers frequently complain that writing is an underdeveloped skill. It's no wonder, when we have an education system that often relegates writing to the land of "I wish we had time" and "That's not on the test." What a tragedy. Teaching students to be effective writers is important by itself. However, writing also provides big gains in reading comprehension and reading fluency.

'Reading is the inhale; writing is the exhale'
Mary K. Tedrow, an award-winning high school English teacher, now serves as the director of the Shenandoah Valley Writing Project. Her book, Write, Think, Learn: Tapping the Power of Daily Student Writing Across the Content Area is available through Routledge:
Writing and reading are intricately intertwined. One is the inverse of the other: Reading is the inhale; writing is the exhale. They depend on each other, and when we find time to practice both, the students are the winners.

In the earliest readers, writing is a natural way to ingest and experiment with a growing knowledge of letters and their function in symbolizing the sounds we speak. Encouraging students to write, even before they know all the rules, builds a deeper understanding of how reading works. In kindergarten, the inventive spelling students employ to compose early writings allows children to represent on the page what they are hearing in the world. Children more clearly understand the letter/sound relationship as they compose thoughts and stories in writing. Recent research has revealed that students who are given latitude to use inventive spelling become better readers (Oulette & Senechall, 2017).

But the interplay between writing and reading goes well beyond just learning to read. When students are asked to write for their own purposes, they intuitively understand the choices authors make as they create a work that moves a reader.

Teachers who have students writing authentically-that is, the way real writers write-can interrupt the process and teach craft lessons. Show students how to develop several good beginnings and ask them to choose the one which serves their purpose best. Show how to incorporate the senses in description, how to move a plot forward through dialogue, how to manipulate sentences for punch and clarity.

All of these writing skills are the inside/out version of analyzing writing by others. When we analyze the books, poetry, and essays we read, we are simply describing the choices an author made on their road to composing a piece. When students are heavily involved in creating those pieces themselves, they will more easily see what authors are doing and understand the messiness required in producing effective communication. Writing brings the author and his or her skill to life.

Students who write are better, more observant, and appreciative readers in general. And students who read are better, more competent writers. Be sure your students have the chance to breathe in and out throughout the day.

Ouellette, G., & Sénéchal, M. (2017). Invented spelling in kindergarten as a predictor of reading and spelling in Grade 1: A new pathway to literacy, or just the same road, less known? Developmental Psychology, 53(1), 77-88. http://dx.doi.org/10.1037/dev0000179

Mary Beth Nicklaus is a secondary-level teacher and literacy specialist for the Wisconsin Rapids public schools in Wisconsin:

Create reading-response writing opportunities focusing on opinions and feelings of the reader. By the time they are in 6th grade, most students want to share information about interests and opinions. How can we connect that interest into reader response? To begin with, we don't always have to work with published text. We can create our own texts in the classroom. We teachers can start the process by writing a letter to students sharing some general information and interests. The teacher then guides the students to write a letter back to them with similar information. This experience encourages students to begin sharing and expressing themselves in writing. Get into the habit of crafting student-writing response assignments for which we are asking about students' feelings and opinions regarding classroom reading-even soliciting poetry writing if that genre works best for some students. Students may also find starting with a salutation hailing a specific audience helps them focus their thoughts in their writing. "Dear teacher/class/partner, I think that ____." They can also focus on sharing their writing with a partner or small group.

Teach the writing process relative to classroom text. Teach students a few writing structures to clearly communicate thoughts and ideas. Teach the main structures of the text you use in your content-be it narrative or expository structures. Let's say we want to teach students to compare and contrast within a classroom text on the running of restaurants. We might use a Venn Diagram graphic organizer to compare and contrast the information about restaurant operation with them on the smartboard. Allow the class to help fill in information. Then together, flesh out a comparison-contrast response with a question like, "Based on our reading today, what might be a more difficult restaurant to run, Culver's or Buffalo Wild Wings?" Use a template to gather student input to flesh out a response. Teach students to support viewpoints with evidence from the text and show them a specific way you will always want them to use to cite evidence. Allow the class to help design or co-create a rubric for evaluating writing, which will help students internalize the elements of the specific writing. Steer the strategy to a similar text where you might use the same kind of structure and response.

Engage in wide practice of written response: Continuing both "big" and "little" writing in our classes, based on the structures and types of texts we teach, can increase reading comprehension. Working on mechanics of writing improves basic reading skills like fluency and word recognition. In addition, continue to practice reading, writing, and reflecting and sharing in whole-group, small-group, and partner contexts. Have students create "Why?" questions to inquire about text. Supply sentence stems to help students focus their text response with their writing such as, "I think ___________ did what he did because in the story_______." Make it a habit of requiring written response in the form of exit response slips where students within a limit of 3-5 minutes, quickly write a response to an inquiry regarding what they learned through the reading. Wide practice of writing helps students' classroom reading become second nature, and it helps prune their focus on text.

'Lure' students into reading through working with their writing

I have found it possible to lure secondary-level students into the reading world through working with their writing. I work with 6-9th grade struggling readers as a reading specialist and literacy coach. By the time they are referred to me, they have not been reading for years-which accounts for much of their struggle. When we teachers work through the power of written self-expression with and for these students, we can also tinker with content-specific academic vocabulary, text structure, and mechanics of writing. We can also prime and build basic reading and comprehension skills. Even researchers have found that use of reading-response writing, explicitly teaching writing process, and engaging students in wide writing practice enhances basic reading skills and comprehension in K-12 readers. Here are some strategies I have found to be successful working with secondary-level students based on the aforementioned three areas:

I know the strategies I have elaborated upon work, because my students made enormous, lasting gains in their reading through focusing on writing. Also, the gains secondary-level students can make through focusing on feelings and opinions in their reading-response writing foster livelier conversations during classroom discussion. Students' overall gains even show students that content texts across the curriculum can pique their interests outside of the classroom. It's a win-win all around!

Having students annotate their writing with the Strategies they use

Colleen Cruz is the author of several titles for teachers, including The Unstoppable Writing Teacher, as well as the author of the young-adult novel, Border Crossing, a Tomás Rivera Mexican American Children's Book Award Finalist. She was a classroom teacher in general education and inclusive settings before joining the Teachers College Reading and Writing Project, where as the director of innovation, she shares her passion for accessibility, 21st-century learning, and social justice. Most recently, Colleen authored Writers Read Better: Nonfiction (July 2018) and Writers Read Better: Narrative published by Corwin:

As an educator who works with teachers and students in grades 2 through 8, I find that I often look at the practices of primary-grade teachers and wish we upper-grade folks borrowed more heavily from them. Whether it be a focus on individual development, an emphasis on play, or just an overarching focus on the whole child, there are pedagogical treasures we need to bring more to our big-kid classrooms. At present, the most pressing for me is the desire to use writing to support reading instruction more often.

Every kindergarten and 1st grade teacher I know asks students to write as soon as they enter the classroom. This is long before students know the entire alphabet or how to read any words. In fact, most of us who have had little ones at home can attest to how often kids pick up a marker or crayon and write their names, strings of letters, or familiar words. Our youngest learners often produce words before they consume them. And when they do that, they are setting themselves up for success as readers because they learn early on, if they can write their name they can read it. If they can write any word, they can read it.

Also, many of us grew up as educators with the knowledge that reading supports writing. I first learned how this conventional wisdom applies to children's writing from Katie Ray and her seminal book Wondrous Words. So, it should not be all that revolutionary to discover that those early-writing and -reading connections still apply when students move into more complex reading.

Show-not-tell in writing helps readers to infer in reading.

Plotting in writing helps readers to make predictions in reading.

Developing objects as symbols in writing helps readers interpret symbols in reading.

Defining a word in writing helps readers to understand the meaning of an unknown word.

Yes, they might have moved past simple decoding and literal comprehension work. But the role of writing and reading reciprocity still applies. For every comprehension move a reader makes, there is an author on the other side of the desk. If a young reader is also a writer, they will be well-positioned to see the mirror moves they have made as a writer in the texts they are reading by other authors. Studies have shown this, of course (Graves, Calkins, Chew, Graham & Hebert to name a few). But in my work with young readers and writers I have seen time and again that if something is challenging to a reader, one of the most accessible paths to overcoming that challenge is through writing. It's a transferable understanding that can last a lifetime: Show students that every reading skill has a reciprocal writing skill, and if they have written something like it, they are able to read it well, too.

One of my favorite ways to do this is to ask students to annotate their writing with the strategies they tried as writers and the reasons why. For example, "I used show-don't-tell in this paragraph to help make a picture in my reader's mind." I then ask them to read a book of their choice with their own writing nearby. When they come to a spot in the text they find challenging, they can look back to their own writing to see if they made a similar move and why. A few common writing/reading reciprocal moves I teach students include:

There are, of course, countless more.
Pam Allyn, senior vice president, innovation & development, Scholastic Education, is a leading literacy expert, author, and motivational speaker. In 2007, she founded LitWorld, a global literacy organization serving children across the United States and in more than 60 countries, pioneering initiatives including the summer reading program LitCamp and World Read Aloud Day:
We know the power of modeling. And I believe for many years, rightly so, we have taught students how to mine the power of the published word for ideas for their own writing. For many of us, it's time to try to teach the power of modeling by asking students to look at their own writing as their mentor for their reading lives. I am hard-pressed to think of more empowering reading work.

Writing 'is a powerful lever for helping our students learn to read profoundly'

Writing and reading are not just two sides of the same coin; they are profoundly related and entwined. I have often said that reading is like breathing in, and writing is like breathing out-the child is taking new breaths in this new world, feeling her power and her potential.
1. Building a deep sense of the beauty of grammar, sound, and vocabulary
Surrounding our children in the sounds of language from literary and informational text is crucial to their understanding of language. The child who is read aloud to multiple times per day, week, month, and year is already realizing the sound and feel of language. Then, too, the child who is given the opportunity to put her first marks on the page is already beginning to make meaning in the world. When reading a book, she sees it as something constructed from a world she already knows because her scribbles connect to those of others and give her the powerful idea that she has a voice.
2. Understanding the purpose of and use of genres
Writing early and constantly, in and out of school, is a powerful lever for helping our students learn to read profoundly. Here are five ways writing supports reading and vice versa:
3. Recognizing the power of writing to connect us
The student who writes becomes alert to the structure of sentences, the rhythm of multiple words together, and words that surprise. Because our students are using the tools of language to build their own stories, they are awake to the qualities of texts. When students share works by authors such as Jacqueline Woodson or Naomi Nye, they're astounded and try to emulate them in their own writing.
4. Becoming aware of the ways writing can change someone's mind or change the world
Students who write quickly learn the necessity of genre. My 1st graders were writing informational texts and choosing their own topics. One wrote about nursing homes because that's where her grandpa was. Later, I saw her scouring a book with a glossary in it. She explained, "I want to add a glossary to my story. My readers might need to know some of the big words I use to describe where my grandpa lives." Genre is already embedded within her at the age of 6.
5. Knowing and deepening one's own writing and the voice of an author
Students who write understand that by telling their stories, they're making their thoughts permanent, which leads to a hearty respect for the text, the authors who write them, and the uses we make of them. When our student writers are finishing works to put into the classroom library, they have an opportunity to see themselves side by side with published works, which feels celebratory. Writing, theirs and others, inspires and connects them.

Even the smallest writer has big ideas. My 2nd grade class once wrote letters to the entire neighborhood inviting them to come see our play. People young and old came, and students saw how they could change their communities with the power of their own words. So, when they read, they consider all the ways writers can change people.
(This is the first post in a two-part series)
The student who writes is building confidence, courage, and a sense of self. She is learning how to evoke emotion, keep someone in suspense, and persuade while developing her own voice, which will serve her in the future whether she's writing a narrative or an email. When she turns to her reading, she is now more aware of the author's voice and knows the risks the author takes. She is one herself.
In what ways can writing support reading instruction?
Thanks to Tony, Mary, Mary Beth, Colleen, and Pam for their contributions.

The new question-of-the-week is:

Consider contributing a question to be answered in a future post. You can send one to me at lferlazzo@epe.org. When you send it in, let me know if I can use your real name if it's selected or if you'd prefer remaining anonymous and have a pseudonym in mind.
If you missed any of the highlights from the first eight years of this blog, you can see a categorized list below.
You can also contact me on Twitter at @Larryferlazzo.
Look for Part Two in a few days.
Education Week has published a collection of posts from this blog, along with new material, in an e-book form. It's titled Classroom Management Q&As: Expert Strategies for Teaching.

I am also creating a Twitter list including all contributors to this column.

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'Writing Directly Benefits Students' Reading Skills' (Opinion)

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Five experienced educators discuss how writing instruction can support the development of reading skills for students throughout K-12.

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Radically transforming language and literacy instruction so every child is on a path to deep reading. #LiteracyandJusticeforALL

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Kagan specializes in staff development and educational resources for teachers. Visit our online store, magazine and message board the leading authority on cooperative learning, classroom discipline and multiple intelligences.

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Neena’s Top Reading Research Picks for April - MetaMetrics Inc.

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Dr. Neena Saha shares recent reading research, including a new study on sustained and spiraled content literacy.

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2024 Monthly Planner - Jan. 2024 - Dec. 2024, 12 Months Calendar/Planner 2024 with Faux Leather, 8.86" x 11.4", 15 Notes Pages, Strong Twin - Wire Binding, Pocket, Monthly Tabs, Perfect Organizer

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Amazon.com : 2024 Monthly Planner - Jan. 2024 - Dec. 2024, 12 Months Calendar/Planner 2024 with Faux Leather, 8.86" x 11.4", 15 Notes Pages, Strong Twin - Wire Binding, Pocket, Monthly Tabs, Perfect Organizer : Office Products

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Accessing Early STAAR Results for District and Campus Administrators (Spring 2024)

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Want to Improve Reading Proficiency? Talk to Kids More

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Education researcher Sonia Cabell explains how effective classroom conversations can boost reading proficiency.

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SyllaSense | Instagram, Facebook | Linktree

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December 2022 Reading Research Recap: Maximizing Small-Group Reading Instruction

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Small Group Reading Instruction | Shanahan on Literacy

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Small group instruction is not superior to whole-class teaching because of the need for independent work--which isn’t particularly effective.

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Blachman, B. A., Ball, E. W., Black, R., & Tangel, D. M. (2000). Road to the code: A phonological awareness program for young children. Baltimore: Brookes.

National Institute of Child Health and Human Development. (2000). Report of the National Reading Panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction (NIH Publication No. 00-4769). Washington, DC: U.S. Government Printing Office.

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Elkonin boxes are used to build phonological awareness skills by having children segment spoken words into their individual sounds (phonemes). To use Elkonin boxes, a child listens to a word and moves a token into a box for each sound or phoneme.

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A place to provide videos for academic interventions and assessments.

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The Kastner Collection: Tools, Resources, and Research Aligned with the Science of Reading

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Explore the articles, videos, and websites to learn more about scientifically-based reading. This Wakelt will have ongoing updates. You may also access every Wakelet from the Kastner Collection at this link: https://wke.lt/w/s/mcU0bZ

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Teacher Professional Learning | Literacy, Math | MTSS

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Curriculum Adoption

Curriculum Implementation

Literacy Coaching & Site Implementation Support

Early Literacy Planning & Implementation Collaboration

Online Language & Literacy Academy

Publisher Approved Professional Learning

Elementary Reading Academy

Adolescent Literacy Solutions

Curriculum Adoption

Curriculum Implementation

Rise in Math

Math Coaching

Online Math Academies + Coaching

Onsite Math Academies

Teaching Reading Sourcebook 3rd Edition

Curriculum Adoption

Curriculum Implementation

Literacy Coaching & Site Implementation Support

Early Literacy Planning & Implementation Collaboration

Online Language & Literacy Academy

Publisher Approved Professional Learning

Elementary Reading Academy

Adolescent Literacy Solutions

Curriculum Adoption

Curriculum Implementation

Rise in Math

Math Coaching

Online Math Academies + Coaching

Onsite Math Academies

Teaching Reading Sourcebook 3rd Edition

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Teaching Beginning Readers to Decode Unfamiliar Words: Connected Phonation Is More Effective than Segmented Phonation - Teacher Professional Learning | Literacy, Math | MTSS

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Curriculum Adoption Curriculum Implementation Literacy Coaching & Site Implementation Support Early Literacy Planning & Implementation Collaboration Online Language & Literacy Academy Publisher Approved Professional Learning Elementary Reading Academy Adolescent Literacy Solutions Curriculum Adoption Curriculum Implementation Rise in Math Math Coaching Online Math Academies + Coaching Onsite Math Academies Teaching Reading

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Decoding Instruction Kindergarten

Grade Level: Kindergarten

School: Kihei Elementary School

Location: Maui, Hawaii

Videographer: Dima Yaremenko
Elapsed Time: 12:39
Introduction: While the goal of reading instruction is always comprehension, systematic decoding instruction must occur in the primary grades to form the foundation for comprehension. We must always remember that decoding is necessary, though not sufficient, for comprehension (Students also need sufficient vocabulary and background knowledge and language skills.) There is no comprehension strategy powerful enough to compensate for the fact you can't read the words.

Focus: As you watch this video,
1) Feedback: 1) Note any good instructional practices. Note any good instructional practices.
o When introducing the initial set of words, the teacher used the following steps with each word.

§ (Writing first letter.) "What sound?"

§ (Writing second letter.) "What sound?"

§ (Running finger under two letters.) "Blend it."

§ (Writing third letter.) "What sound?"

§ (Running finger under three letters.) "Blend it."

o When rereading the list of words, the teacher again used an instructional routine so that all "cognitive energy" could be placed on the words not the task.

§ (Pointing to word.) "Sound out this word. Put your thumb up when you know the word."

§ "Whisper that word to your partner." (Teacher monitors individuals.)

§ (Tapping on word.) "What word?"

§ (Erasing word.) "Gone word."

Reread and sentences to increase automaticity on the words.

Stressed meaning even as words are being read

Modeled "curiosity". "I wonder...."

Gained responses from students (choral responses, partner responses, thumbs up, acting out).

Supported the English Language Learners through clear language, repetition, gestures, and demonstrations.

Maintained a positive climate.

o Smiled and nodded at students.

o Verbally acknowledged them.

o Reinforced them with "Gone Word" game and Special Clap.

Put the back row of children in chairs so that they could see the instruction.

Delivered the instruction in an engaging manner.

Conveyed the joy of teaching and learning.

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Explicit Instructions | Effective and Efficient Teaching

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Grade Level: Kindergarten School: Kihei Elementary School Location: Maui, Hawaii Videographer: Dima Yaremenko Elapsed Time: 12:39 Introduction: While the goal of reading instruction is always comprehension, systematic decoding instruction must occur in the primary grades to form the foundation for comprehension.

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Effective and Efficient Teaching

By Anita L. Archer and Charles A. Hughes

Explicit instruction is systematic, direct, engaging, and success oriented-and has been shown to promote achievement for all students. This highly practical and accessible resource gives special and general education teachers the tools to implement explicit instruction in any grade level or content area. The authors are leading experts who provide clear guidelines for identifying key concepts, strategies, skills, and routines to teach; designing and delivering effective lessons; and giving students opportunities to practice and master new material. Sample lesson plans, lively examples, and reproducible checklists and teacher worksheets enhance the utility of the volume.

This title is part of the What Works for Special-Needs Learners Series, edited by Karen R. Harris and Steve Graham.

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Explicit Instructions | Effective and Efficient Teaching

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By Anita L. Archer and Charles A. Hughes Explicit instruction is systematic, direct, engaging, and success oriented-and has been shown to promote achievement for all students. This highly practical and accessible resource gives special and general education teachers the tools to implement explicit instruction in any grade level or content area.

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Share your videos with friends, family, and the world

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Grit: the power of passion and perseverance | Angela Lee Duckworth

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Learning tuesdays | The Science of Reading and Emergent Bilinguals

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Reading Assistance for STAAR Grade 3 Math 23-24

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Description

NCIL, in collaboration with BU, is hosting a series of live webinar discussions on the science of reading, literacy instruction, and literacy-related policies that affect students and teachers.

The Reading the Room series brings together education stakeholders with a shared interest and expertise in improving literacy outcomes for students. This Reading the Room panel will discuss how to sustain your Multi-tiered Systems of Support for Reading (MTSS-R). School district leaders and implementation researchers will come together to share their perspectives on the factors and characteristics which are consistently associated with sustaining implementation of MTSS-R. Panelists will discuss how practitioners can sustain a strong MTSS-R that supports all students' reading development.

Participants will receive access to a variety of free, evidence-based tools and resources, including NCIL's online MTSS-R Implementation Guide.

MODERATOR:

Lana Santoro, Ph.D., Research Professor, Boston University Wheelock College of Education and Human Development

PANELISTS:

Jason Borges, M.Ed., Executive Director of Literacy and Academic Interventions Services, NYC PS Literacy Collaborative

Melissa Collier-Meek, Ph.D., Associate Professor of Psychology and Education, Columbia University

Nathan Clemens, Ph.D., Professor, Department of Special Education, The University of Texas at Austin

More from this Series
Reading the Room Discussion Series

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Sustaining your Multi-tiered Systems of Support for Reading

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Watch this recorded discussion on the science of reading, literacy instruction, and literacy-related policies that affect students and teachers.

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As an initiative of the U.S. Department of Education’s Institute of Education Sciences (IES), the What Works Clearinghouse (WWC) was created in 2002 to be a central and trusted source of scientific evidence for what works in education.

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Virtual Blending Board

The Virtual Blending Board should be used in most lessons during Step 4: Blending Drill. The Virtual Blending Board is designed for the teacher to project and manipulate, while students build their decoding automaticity by reading the words the teacher forms. To set up the Virtual Blending Board for a lesson, use the grapheme grid provided in the lesson plan to identify the letters you'll need. Then, use the word chain provided to build words for your students to decode. The tutorial video provides more detailed information for how to set up and use the Virtual Blending Board.

Virtual Blending Board

Virtual Blending Board Tutorial

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The Virtual Blending Board should be used in most lessons during Step 4: Blending Drill. The Virtual Blending Board is designed for the teacher to project and manipulate, while students build their decoding automaticity by reading the words the teacher forms. To set up the Virtual Blending Board for a lesson, use the grapheme grid provided in the lesson plan to identify the letters you'll need.

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The Importance of Dual Language Assessment & How to Deliver It | Amplify mCLASS Lectura

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The Importance of Cross-linguistic Transfer in K–8 Bilteracy Instruction and Learning | Amplify

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What is dyslexia? - Kelli Sandman-Hurley

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For parents applying to the dual-language program at Rochester, New York's public school No. 12, where students learn in both English and Spanish, the process can be both bureaucratic and baffling. After listing the program as a top choice, parents must schedule a testing appointment at the central office, where an instructor gauges such skills as whether each incoming kindergartener can hold a book properly and turn its pages, identify that a sentence is made up of words and spaces, use words to describe the scene in a picture, identify sounds in a word, and other pre-reading skills.

Families never receive a "score" on the test, which is available in both English or Spanish, or any information about how it is used in the admissions process - just word on whether their child made it in. (The district communications office did not respond to multiple queries about the process.)

After her 5-year-old son took the test several years ago, Rochester parent Llerena Searle was convinced that the news wouldn't be good. He had a meltdown when asked to go with an unfamiliar instructor, acquiescing only when allowed to "test" from his mother's lap. The boy was admitted, though, and is now in seventh grade; Searle believes he received a wonderful education at school No. 12. "I just wish it were more accessible," she said.

In some communities across the country, dual-language programs - one of the best means of ensuring equity for underserved groups, especially English learners - have taken an elitist turn. And with the Biden administration eager to help districts expand such programs, questions about who they help - and who gets left out - are becoming more urgent.

In too many places, admissions processes send a message that dual-language learning is not for everyone (when research shows that actually it is). In Mamaroneck, New York, for instance, the local dual-language school at one point published information asking families to consider whether their child's native language is developing within "normal" limits when deciding whether to apply. (After this article published, school officials reached out to say that has not been their practice for some time, and the program is open to all interested families.) In Boston, the dual-language programs significantly under-enroll students with disabilities, partly out of a misconception that learning in two languages isn't appropriate for many students with special education needs.*
Related: A Spanish-English high school proves learning in two languages can boost graduation rates
In other districts, the sin is one of omission rather than commission: failure to market the dual-language programs sufficiently to newcomer families; failure to locate the programs in communities where newcomers actually live; time-consuming admissions processes that can seem labyrinthine and opaque - even if they don't involve testing recalcitrant preschoolers.

Most experts recommend reserving at least half of seats in dual-language programs for English learners, who benefit most from programs partly in their native language, and dividing the remainder through random lottery after aggressive outreach to underrepresented communities, including Black families, low-income students and those with disabilities. Yet English learner enrollment shares are shrinking in most dual-language schools in large cities including New York, Los Angeles and San Francisco, according to a report released last spring by The Century Foundation and the Children's Equity Project.

Meanwhile, the share of white student enrollment was up in several other cities, most noticeably Washington D.C. "Many dual-language programs are at risk of tilting toward language enrichment for English-dominant children, instead of advancing linguistic equity and expanding educational opportunity for ELs," the report's authors wrote. Overall, the number of dual-language schools in the country has nearly quadrupled since 2010, and currently numbers more than 3,600.

"[P]rograms that were ostensibly created to help English learners have turned into an extracurricular for native English speakers."
Alina Adams, parent

There's no one solution to this troubling shift - dual-language programs are gentrifying in many cities partly because the cities themselves are gentrifying. In some communities, English learner enrollments are depressed because of the lingering effects of hypocritical policies in the U.S. banning bilingual education for non-English speaking newcomers. Many immigrant families absorbed the "English only" message, and remained hesitant to try dual language even after the policies changed.

But school districts need to be far more vigilant in designing admissions processes and programs that favor the least privileged rather than the most. Otherwise, one of the most proven ways to combat the achievement gap, particularly for English learners, is at risk of playing a perversely opposite role: expanding educational opportunity for the elite.

Dual-language programs have never been monolithic in their demographics or their goals. When they began to appear in significant numbers in the U.S. in the 1980s and 1990s, some opened with the intent of serving English learners and working-class Latino families. Others hoped to enroll a significant number of white, English-speaking families, and even deter white flight from urban areas. Some wanted to meet both goals. One-way language schools enroll predominantly students from a single language group, while most two-way programs try to enroll a roughly equivalent number of students from English-speaking households and the target language.

Widespread gentrification in the 1990s and early 2000s also brought many white and well-off families back to some urban neighborhoods where dual-language schools were taking root. That coincided with a growing recognition by privileged families of the economic and career benefits of bilingualism, and a particular interest in affluent communities in studying Spanish and Mandarin. Research shows that learning multiple languages early in life has cognitive benefits extending beyond language acquisition and helps children develop stronger social skills, including empathizing better with others. In sum, bilingualism is good for both the brain and the heart.

In New York City, meanwhile, some middle-class and affluent families have come to see dual-language programs as an alternative to gifted and talented education, particularly as the latter has become harder to access, said Alina Adams, a parent and creator of the website NYCSchoolSecrets.com. Over the last decade, "gifted and talented became more competitive every year and suddenly there were many more dual-language programs," she said. Ambitious parents perceived it as a more rigorous, challenging curriculum. And at some locations, "programs that were ostensibly created to help English learners have turned into an extracurricular for native English speakers," Adams added.
Related: Students with disabilities often left out of popular 'dual-language' programs
Yet recent decades have also brought a growing research base showing that it's precisely the students least likely to seek out gifted and talented programming who can benefit most from well-designed, supportive dual language programs. "Dual language is the one program we've found that truly closes the [achievement] gap" between English learners and the rest of the student population, said Virginia Collier, an emeritus professor of education at George Mason University. Her research, done over the course of four decades in collaboration with her husband and GMU colleague Wayne Thomas, also shows that dual-language learning can be particularly effective for Black students, low-income students, and those with special needs - three groups that are often underrepresented in the programs.

There's a misconception among some educators and parents that bilingual education is inappropriate for students with developmental delays, or those predisposed to fall behind in an English-only curriculum. Yet a 2021 study found that dual-language "education can benefit ... even students who often struggle in school because of special education needs." And a 2018 paper found "no credible evidence that bilingual education adds or creates burden for children. Yet it is "incontrovertible," according to the paper, that bilingual learning comes with decided advantages.

Spanish dual-language programs, the most common kind in the U.S., can be especially beneficial for students who struggle with reading. That's because the Spanish language is more phonetic than the English one, with much less variation in the sounds that letters make. But some programs send the message - whether intentional or accidental - that dual language schools aren't appropriate for children without strong early literacy skills.

"You might hear a parent say, 'My kid didn't start talking until age three and a half. They are already struggling - it would be too confusing to be in a dual language program,'" said Emily Bivins, former principal of a dual-language school in Chapel Hill, North Carolina whose company provides professional development for dual-language programs. "We all know the research is counter to that. These are the students who absolutely need to be in our bilingual programs."

Bivins' own three children attended dual-language programs, and she said it was most helpful for the child with an attention deficit diagnosis and early reading struggles. "Learning to read in Spanish was much better for her ... the rules were clearer," Bivins said. That's part of the reason it's so frustrating when she hears from colleagues at dual-language schools that use reading screeners where, if students "don't score high enough [they] don't get in."

Widespread interest in dual-language schools, including among the affluent, is a good thing, say proponents of bilingual education. But it becomes problematic if students from underserved groups are neglected or squeezed out of programs. Many communities lack sufficient bilingual educators to meet the desire for dual language. "It's an iron law of education policymaking: nothing exacerbates educational unfairness like scarcity," wrote the authors of the report released last spring.

The history of the Amigos School, a dual-language program in Cambridge, Massachusetts, shows that even seemingly minor changes to admissions processes can significantly shape how a school is perceived - and who applies - tilting preference toward privilege.

Thirty-five years ago, scores of first- and second-generation immigrant families from Puerto Rico and the Dominican Republic, along with others, came to see Amigos as the place to send their kids. The school was located near subsidized public housing, where many of the families lived. And the school's founder, Mary Cazabon, engaged in constant grassroots outreach, attending community events and churches, like Cambridge's bilingual Saint Mary's church, where she spread word about the school and the benefits of learning in two languages. "We wanted to make sure that we were going to address the needs of the students who were most vulnerable," Cazabon says. "The priority was on them." To that end, Spanish-speaking students designated as English learners were given priority in admissions, Cazabon says.

Then the biotech boom hit Cambridge in the 1990s, and a growing number of white and wealthier families began to take an interest in Amigos, drawn by the allure of raising bilingual children. At some point in the 2000s, the school district also made a pivotal switch: Instead of giving priority to English learners, as Cazabon had done, they introduced a system that awarded "Spanish points" to children who could show some knowledge of Spanish when applying to the school's pre-K or kindergarten.
Related: Once criticized, 'Spanglish' finds a place in the classroom
The change opened the door to a much broader group of families gaining admissions preference: Families with some Hispanic heritage whose toddlers were exposed to both English and Spanish in the home, but also families with no Hispanic heritage who sent their children to a Spanish-language child care or hired Spanish-speaking nannies with the goal of getting a spot at Amigos. By 2010, the demographics of Amigos had shifted dramatically, and it enrolled fewer low-income students than almost all the schools in the district. Penn Loh, a lecturer at Tufts University, said that in his son's class at that time, only two of 44 children qualified for free and reduced lunch.

In 2011, one mother filed a complaint with the Cambridge Human Rights Commission, alleging that Amigos no longer served the Hispanic community. And Loh and other parents at Amigos petitioned the school board to change the admissions process, worried that Amigos increasingly catered too much to the children of Cambridge's elite. "The pool of Spanish-proficient applicants became more unbalanced, with more wealthy, privileged families having children qualify in this pool," Loh said in a recent email.. "We heard that working class Latinx families, often in Cambridge for generations, were not ... getting into the school."

The school district changed the policy to give "points" to children from lower socioeconomic backgrounds.

The number of dual-language public schools in the U.S. has quadrupled since 2010, to more than 3,600.

"We are on our way to being much more balanced," said Sarah Bartels-Marrero, the school's current principal. "To me, it's very important that we have a very diverse group of Spanish-speaking students. That's a core pillar of our school." The Spanish points system helps ensure that, she added, although she acknowledged that some English-only parents have also employed it as a workaround. "Certain individuals with privilege and knowledge may look for a loophole," she said. "That is a thing, but we work really hard to combat and mitigate that."

Amigos continues to enroll slightly fewer English learners and about 10 percent fewer low-income students than the district average. Although the current formula would virtually guarantee a low-income Spanish speaking student admission, only one such incoming kindergartener listed Amigos as their first choice in January 2022, according to data published by the district. However, Bartels-Marrero pointed out that about 60 percent of families identify as Hispanic or Latino, a group that is incredibly diverse. "To me it's fundamentally important that [Amigos] is an option and opportunity for every kid in Cambridge regardless of race or background," she said.

Some states and communities also suffer from a location problem when it comes to dual language. The predominantly white town of Maynard, Massachusetts created a Spanish dual-language school with its English speakers in mind - not its growing population of Portuguese-speaking students, for instance. But the thousands of Spanish-speaking English learner students in the much larger and heavily Hispanic city of Lawrence, located just 35 miles to the north, have for two decades lacked access to even a single dual-language Spanish program (two are slated to open in the next year or so). States and the federal government could, and should, incentivize districts to open programs where there is the most need, and discourage programs targeted mostly at English speakers.

But starting new programs takes time, and there are steps that school districts can take right now to help ensure that English learners, low-income students, Black students, and other underrepresented groups have equal, if not greater access, to dual-language programs. They should engage more in grassroots outreach and marketing of dual learning, tailoring the message as needed to different communities. They should make the admissions process as transparent and accessible as possible, avoiding complicated or burdensome steps that advantage those with flexible schedules and knowledge of school system bureaucracy.

And they should eschew any kind of elitist framing, intentional or not.

Llerena Searle, the Rochester mother, liked the dual-language program at School No. 12 well enough to enroll her younger child there, too. This time, there was a pandemic going on and the child was tested over Zoom. Her daughter dutifully cooperated with the process. With little doubt of a successful outcome (the school also has an admissions preference for siblings) Searle was more relaxed this time, yet hardly sanguine about the admissions process. She never figured out exactly what district officials were trying to accomplish, but in the end worried that the test mostly measured privilege.
*Clarification: This article was updated to reflect the fact that the dual language program in Mamaroneck, New York, is now open to all interested families, including those with disabilities.
This story about dual language programs was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for the Hechinger newsletter

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