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Malik and Rehman (2012) state, "Quantitative analysis revealed that academic interventions were highly effective in enhancing the developmental skills of slow learners' adaptive, communication and cognitive developmental skills' (pg. 135).

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This page contains short training videos by Jim Wright on intervention and assessment topics.

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Index of Strategies for Teaching Social Studies

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End of Cluster Expectations | Social Studies Literature | Field Trips | Professional Development Opportunities | Internet Resources Articles | DSSEP Home Page | DCTE Home Page | Join Our Listserv | Social Studies/Reading Workshops | Key Delaware Benchmark Terms | Strategies for Teaching Social Studies

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Teacher Strategies to Promote Learning | Intervention Central

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8. Periodic Review. Once students have mastered a particular academic skill, the instructor will quickly move them on to a more advanced learning objective. However, the teacher should make sure that students retain previously mastered academic skills by periodically having them review that material.

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April Bondor     Possible Forms of Assessment

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April Bondor     Possible Forms of Assessment

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Multiple Choice
Student Observation
Short Answer

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April Bondor Intervention Strategies to Consider

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April Bondor Intervention Strategies to Consider

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1. Direct Instruction
2. Small Group/One-On-One Instruction
3. Peer Tutoring
4. Ability Grouping
5. Repeated Practice
6. Progress Monitoring
7. Technology

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April Bondor             Ideas to Consider

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April Bondor             Ideas to Consider

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1. Students need to be able to apply math number line concepts to place events on timeline.
2. Students need to be able to think critically in order to draw conclusions about event placement on timeline.
3. Intervention Strategies need to help students with math concepts as well at with critical thinking.

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This article gives approaches that would enable students to be successful with critical thinking skills in Social Studies. The results included classroom discussions, writing activities, and asking questions as necessary to foster critical thinking skills.

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This article explains how interventions can help gifted learners. The results of the study showed that gifted learners benefited from compacting the curriculum, cluster grouping and problem-based instruction.

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According to Maxwell (2010), " The in-class essay is not an effective means to assess student ability in a history exam. History teachers should instead ask short-answer questions in order to test what the American Historical Association calls 'objective' knowledge: the ability to identify concepts, historical actors, organizations, events, and so forth" (pg. 233).

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This is a case study about the effect of Data-Based Instruction on a student with a disability. Examples of principles and processes of intensive intervention are given.

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This article explains the goals of Response to Intervention. It explains that children with learning disabilities perform better when specific academic skills are addressed.

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Children struggle to learn reading, mathematics, and writing skills for many reasons, including growing up in economically disadvantaged settings, low proficiency in English, emotional difficulties, and even inadequate academic instruction ( Donovan & Cross, 2002). Some children are eventually identified with learning disabilities (LDs), representing approximately 5% of the school-age population and approximately 50% of students identified with disabilities in schools ( U.S. Department of Education, 2007).

A variety of state, federal, and district school-based programs attempt to address different obstacles to learning academic skills. With the 2002 passage of the No Child Left Behind Act, which targets the needs of economically disadvantaged children through Title I funding, the federal government placed greater emphasis on early intervention, high-quality instruction, and accountability for academic outcomes. In 2004, the Individuals with Disabilities in Education Act (IDEA; U.S. Department of Education, 2004), which governs the provision of special education services in U.S. public schools, was also reauthorized.

Noteworthy in the reauthorization was the emphasis on early intervention services and specific provisions allowing districts to adopt service delivery models that focus on the child's response to intervention (RTI). These models (a) screen all children for academic and behavioral problems; (b) monitor the progress of children at risk for difficulties in these areas; and (c) provide increasingly intense interventions based on the response to progress monitoring assessments ( Vaughn & Fuchs, 2003). Those children who do not respond adequately may be referred for a comprehensive evaluation for eligibility for special education services. Through the comprehensive evaluation, some children will be eligible for special education and others may need alternative services because their difficulties in learning are not due to an LD or other type of disability consistent with a need for special education.

Service delivery models that provide universal screening, progress monitoring, and tiered, or layered, interventions have been widely adopted in No Child Left Behind and Title I and are a specific focus of IDEA 2004. We discuss the research and policy basis for these models, focusing only on academic difficulties (primarily reading) and noting that there is comparable development of RTI models in the behavioral area ( Walker, Stiller, Serverson, Feil, & Golly, 1998).

Multitiered Intervention Models and RTI

What Are RTI Models?

RTI models are multitiered service delivery systems in which schools provide layered interventions that begin in general education and increase in intensity (e.g., increased time for instruction to smaller groups of students) depending on the students' instructional response. There are many approaches to the implementation of RTI models, which are best considered as a set of processes and not a single model, with variation in how the processes are implemented. These approaches have at least two historical origins, both representing efforts to implement prevention programs in schools.

The first source involves schoolwide efforts to prevent behavior problems ( Donovan & Cross, 2002; Walker et al., 1998). These models are associated with a problem-solving process in which a shared decision-making team identifies a behavior or academic problem, proposes strategies that address the problem, evaluates the outcome, and then reconvenes to consider whether the problem has been resolved, leading to improvements in behavior or learning ( Reschly & Tilly, 1999).

The second origin derives from research on preventing reading difficulties in children. These approaches typically use standardized protocols to deliver interventions increasing in intensity and differentiation depending on the child's instructional response. Both models have been significantly influenced by public health models of disease prevention that differentiate primary, secondary, and tertiary levels of intervention that increase in cost and intensity depending on the patient's response to treatment ( Vaughn, Wanzek, & Fletcher, 2007).

In a common implementation of a standard protocol model (; Vaughn, Wanzek, Woodruff, & Linan-Thompson, 2006), all students are screened and those at risk for academic problems are assessed frequently (every 1-4 weeks) on short probes designed to assess progress over time ( Stecker, Fuchs, & Fuchs, 2005). Classroom teachers receive professional development in effective instruction and ways to enhance differentiation and intensity through flexible grouping strategies and evaluations of progress (Tier 1, primary intervention). Children who do not achieve specified levels of progress based on local or national benchmarks receive additional instruction in small groups of three to five students for 20-40 minutes daily (Tier 2, secondary intervention). If the child does not make adequate progress in secondary intervention, an even more intensive and individualized intervention (Tier 3, tertiary intervention) is provided that may involve smaller groups, increased time in intervention (45-60 minutes daily), and a more specialized teacher. Progress is monitored weekly or biweekly. These models link with special education because inadequate instructional response allows for determination of adequate and inadequate responders and provides a framework for implementing seamless interventions between general and special education.

The implementation of both problem-solving and standardized protocol models is a significant effort. First, providing effective Tier 1 instruction to all students requires ongoing professional development, screening, and progress monitoring of students. Maintaining these practices demands an extensive professional development regiment from well-trained and committed professionals that are not readily available ( NASDSE, 2006). Second, Tier 2 intervention is continuous. Although effective Tier 1 reduces the number of students at risk, significant numbers of students (as many as 20%-25% in early reading; Vaughn et al., 2006) require supplemental interventions by trained personnel (e.g., classroom teachers, paraprofessionals). Finally, many school districts do not perceive that they have the personnel and resources to effectively implement all of the elements of RTI models. Nonetheless, over the past 20 years, many school districts have implemented RTI models from kindergarten to high school ( Jimerson, Burns, & VanDerHeyden, 2007).

Screening and Progress Monitoring

A key component of RTI models is universal screening of all children for academic problems. The screening instrument can be norm-referenced or criterion-referenced, the latter often representing the first assessment of a progress monitoring tool. Because screening devices are used with entire grades, the key is that the tool can be quickly administered with adequate sensitivity and specificity. In general, screenings tend to overidentify children as being at risk because the consequence is that students' progress is monitored and/or they are provided a supplemental intervention to enhance their performance in reading or math ( Fletcher, Lyon, Fuchs, & Barnes, 2007).

The most common implementation of a progress monitoring measure involves a technology known as curriculum-based measurement (CBM), which provides brief (1-3 minutes per child) assessments that are readily administered and interpreted by classroom teachers and useful for adjusting instruction ( Fuchs, Deno, & Mirkin, 1984). Typically, a child reads a list of words or a short passage appropriate for his or her grade level (or does a set of math computations, spells words, etc.). The number of words (or math problems or spelling items) correctly read (or computed or spelled) is graphed over time and compared against grade-level benchmarks.

There is a substantial research base showing that, when used by classroom teachers, CBM provides reliable and valid information about how well students are progressing and is associated with improved outcomes ( Stecker et al., 2005). Controlled studies document that when CBM implementation is compared to classrooms not using CBM, better end-of-year academic outcomes result because teachers modify goals and adjust instruction (e.g., Fuchs, Fuchs, & Hamlett, 1989a, 1989b; Fuchs, Fuchs, Hamlett, & Stecker, 1991). Serial assessments based on CBM have also been used to provide data for eligibility decisions involving education services ( Fuchs & Fuchs, 1998).

Despite the value of CBM measures, there are concerns about equivalency of text passages ( Francis et al., in press). In addition, it is unclear how reliably benchmarks from these CBM-type measures can be used to determine movement across the tiers, and when used, whether the best benchmarks are at the district or national level. Finally, the use of CBM measures as part of the eligibility process is especially controversial, and there are no widely accepted criteria for identification of inadequate responders. Thus, instructional response is not recommended as the sole determinant of eligibility for special education.

Evidence-Based Interventions

RTI models depend on the implementation of evidence-based interventions designed to prevent or remediate academic difficulties. Numerous syntheses and meta-analyses address the efficacy of interventions for students with academic difficulties. Although a complete analysis is beyond this article, the most comprehensive meta-analysis on interventions for children identified with LDs was conducted by Swanson, Hoskyn, and Lee (1999), who reviewed and analyzed 180 intervention studies over a 30- year period. Their findings suggested moderate to high effects across studies (0.79) and higher effect sizes for interventions conducted in resource room settings (0.86) than those in general education classes (0.48).

Wanzek and Vaughn (2007) synthesized studies of extensive reading interventions defined as at least 100 sessions (i.e., approximately 20 weeks of daily intervention). The effects were diverse, but most studies reported effect sizes in the moderate to large range. Effect sizes were usually larger if the study (a) involved students in kindergarten and grade 1 as opposed to grades 2-5, (b) used a comprehensive reading program, and (c) delivered the intervention one-on-one or in small groups.

In another recent meta-analysis, Scammacca et al. (2007) examined outcomes from intervention studies conducted with older students with reading difficulties. The overall effect size across all 31 studies in Scammacca et al. (2007) was 0.95, with a lower overall effect size when only standardized, norm-referenced measures were used (0.42). For 23 intervention studies that measured reading comprehension, often with experimenter-designed measures, the effect size was 1.33; for standardized achievement reading measures, the effect size was 0.35. The overall findings indicate that for older students with reading difficulties (a) adolescence is not too late to intervene, (b) students benefit from both word-level and text-level interventions, (c) instruction in reading comprehension strategies is associated with large effects, (d) students are able to learn the meanings of words they are taught, and (e) both researcher-implemented and teacher-implemented interventions are effective. However, it may take more intensity and a longer period of time to bring older children with reading difficulties to grade level, which is why prevention efforts are receiving greater emphasis ( Torgesen et al., 2001).

Graham and Perrin (2007a, 2007b) provided meta-analyses on effective writing practices. They identified several instructional practices that are associated with improved outcomes for students, including (a) writing strategies that involve explicitly teaching students to plan, revise, brainstorm, and edit (0.82); (b) summarizing through writing (0.82); (c) collaborating with other students in small groups to provide feedback and write cooperatives (0.75); (c) assigning students reasonable goals for improving writing (0.70); and (d) other practices such as word processing, sentence combining, and writing process, which all yield small to medium effect sizes.

There is less intervention research in the academic area of mathematics, although recent research implementing RTI-type frameworks is promising ( Fuchs et al., 2005). Baker, Gersten, and Lee (2002) completed an empirical synthesis that revealed that effective mathematics instruction provides data or recommendations to teachers and students (0.57), uses peer-pairing to support learning (0.62), provides explicit instruction directed by the teacher including teacher-facilitated approaches (0.58), and provides practices for communicating student successes to parents (0.42).

Coordinated Systems of Service Delivery

Despite the research base supporting the assessment and intervention components of RTI, the most daunting aspects involve schoolwide implementation, where the scaling issues are significant. Intervention services in schools are often funded by separate entitlement programs, especially Title I and IDEA, that tend to have specific eligibility criteria and historically have made it difficult to blend resources to support schoolwide intervention models. These programs are often isolated from general education and the classroom, so that instruction can be fragmented. Because it may take several years to change practice in implementing RTI models, especially given the entrenchment of older ways of thinking about instruction, schools should move slowly and with care ( NASDSE, 2006). Resources will be a concern in many districts unless careful appraisals are made of the available resources, which are typically redeployed to support RTI models of service delivery. A negative consequence of the fact that RTI is recommended under the new reauthorized IDEA 2004 is that many educators perceive RTI as simply a special education initiative. In fact, implementation of RTI models requires close collaboration and implementation with general education, special education, Title I, and other entitlement programs.

Scaling issues are also complicated because of incompleteness in the intervention evidence base. The question of how to implement RTI models in secondary schools is daunting, especially given weaknesses in research studies on interventions and progress-monitoring tools for older students. It seems difficult to conceive of RTI models when the prevention component is not strongly implemented. Although there are data on intensive Tier 3 interventions, they have been infrequently applied as part of a multitiered intervention, reflecting in part the sheer cost of research studies based on a multitiered intervention model. Because few studies have been done of children defined as inadequate responders in an RTI model, the efficacy of a layered Tier 3 intervention in this context is not well established. Preliminary evidence suggests that many of these students are difficult to teach, with approximately half showing insufficient progress to read at grade-appropriate levels even after receiving a yearlong intervention followed by additional intense interventions (16 weeks) in grades 1-3 ( Denton, Fletcher, Anthony, & Francis, 2006). However, studies of multitiered intervention models yield rates of inadequate responders for early reading as low as 2%-5% ( Berninger et al., 2003; Mathes et al., 2005; McMaster, Fuchs, Fuchs, & Compton, 2005; Torgesen, 2000). Because the number of students who need intense interventions may be greatly reduced, schools may be able to devote the resources needed for effective remediation of inadequate responders ( Burns, Appleton, & Stehouwer, 2005; VanDerHeyden, Witt, & Gilbertson, 2007).

Despite these issues, there are successful district-wide implementations of RTI models across the country ( Jimerson et al., 2007; NASDSE, 2006). Many of these implementations report an increase in overall academic achievement scores and a decrease in special education referrals (e.g., VanDerHeyden et al., 2007). More research focusing on how schools successfully implement (and struggle to implement) RTI models will be needed. This research must look at outcomes in relation to historical data so that it will be clear that RTI models improve outcomes for all students, including those who are at risk and not at risk for academic difficulties. Furthermore, scaling these models nationally will be a significant task.

RTI Models and Special Education

The specific provisions in IDEA 2004 for RTI models have been controversial in the area of special education. This controversy focuses primarily on two issues, the first representing the scaling issues reviewed above. The second issue is the use of RTI models in the identification of LDs. In contrast to 30 years of implementation, IDEA 2004 allows school districts to implement RTI models and move away from identification models that have relied on a discrepancy between IQ and achievement. Instead, identification relies on inadequate instructional response and other criteria, minimizing the role of IQ and other assessments attempting to identify discrepancies in cognitive ability for identification. Given how entrenched the latter assessments are in the everyday practice of evaluating students, this controversy is not surprising. However, the changes in IDEA 2004 reflect concerns about (a) the effectiveness of traditional implementations of special education in schools and (b) the use of IQ-achievement discrepancies for identification.

Intervention

There is a major disconnection between what is known about efficacy of instruction for students with academic difficulties and how students are taught in schools, especially for students most at risk for academic and behavioral difficulties. Studies of outcomes for students placed in special education show flat levels of growth and little evidence that typical interventions close the achievement gap ( Bentum & Aaron, 2003; Donovan & Cross, 2002; Glass, 1983; Hanushek, Kain, & Rivkin, 1998; Torgesen et al., 2001; Vaughn, Levy, Coleman, & Bos, 2002). The interest in RTI is fueled in part by the focus on instructional outcomes and the attempt to reduce the number of students who need the most intense intervention. As we reviewed above, the most consistent evidence about improving outcomes for students identified with LDs addresses preventing or remediating specific academic skills, where the focus on academic domains is especially important ( Fletcher et al., 2007). These children are subjected to a potpourri of interventions involving the eyes, brain, and perceptual processes that do not involve reading, writing, and math. The former interventions show little generalization to academic successes for these students ( Mann, 1979; Vellutino, Fletcher, Scanlon, & Snowling, 2004).

Identification

Although deficits in specific cognitive functions are strongly associated with different types of LD, a focus on interindividual differences and discrepancies has not proven to be a reliable practice for identification ( Francis et al., 2005; Shepard, 1980; Stuebing et al., 2002; Siegel, 1992) and does not lead to implementation of appropriate interventions resulting in strong outcomes ( Mann, 1979; Vaughn & Linan-Thompson, 2003). These concerns are especially significant for the use of IQ-achievement discrepancy models of identification.

Two meta-analyses highlight the concerns about the validity of IQ-discrepancy models ( Hoskyn & Swanson, 2000; Stuebing et al., 2002). Across studies, both found negligible to small overall effect size differences between IQ-discrepant and nondiscrepant poor readers, with negligible differences on most measures of reading and phonological processing. Other studies comparing poor readers with and without significant IQ-achievement discrepancies find no difference in prognosis ( Francis, Shaywitz, Stuebing, Shaywitz, & Fletcher, 1996; Share, McGee, & Silva, 1989) or response to instruction ( Vellutino, Scanlon, & Lyon, 2000). These validity issues do not support the 30-year-old practice instantiated in schools and clinics of identifying LDs on the basis of a discrepancy between IQ and achievement ( Donovan & Cross, 2002).

RTI Models and Identification

RTI models specifically de-emphasize cognitive discrepancies in the identification process, focusing instead on discrepancies relative to age-based expectations and instruction. Thus, the eligibility process in an RTI model is different from the traditional model (see ). In an RTI model, children have been screened and monitored early in schooling as opposed to traditional eligibility models that depend on referral, usually in the later grades and after failure. In addition, the data on instructional response lead to evaluations that ask how to best teach the child and that deemphasize the search for cognitive discrepancies.

The use of instructional response data, however, is unlikely to address all problems related to identifying students with LDs. Within IQ-discrepancy models, one of the persistent problems has been the use of rigid "cut points" for identification of LDs. The use of rigid cut-points for benchmarks and establishing students as high or low responders to instruction could yield the same types of problems with reliability and validity of identification in RTI models. A problem with cognitive discrepancy models is that the attributes (IQ, cognitive processes, achievement) are usually continuous and normally distributed when children with brain injury are excluded from the sample ( Lewis, Hitch, & Walker, 1994; Shaywitz, Escobar, Shaywitz, Fletcher, & Makuch, 1992; but see Rutter & Yule, 1975, which did not exclude brain injury). Deciding reliably where on this continuum a disability resides is inherently arbitrary and must rely on criteria other than IQ and achievement scores ( Francis et al., 2005). However, instructional response may also exist on a continuum that has no inherent qualitative breaks. Criteria for inadequate response may be as arbitrary as a cut-point on an achievement dimension, and simply creating formulae without testing their validity will be no better than IQ-discrepancy models. The use of confidence intervals and an evaluation of the consequences of different decisions to intervene or not intervene will help with this issue. Validating decisions against other adaptive criteria not directly tied to academic achievement would also help determine the adequacy of decisions. Research is also needed to evaluate the reliability and validity of decisions made by experts and not decisions based solely on statistical criteria.

LD Identification Requires Multiple Criteria

Children cannot be identified with LDs solely on the basis of instructional response. The consensus group of researchers convened for the Learning Disabilities Summit ( Bradley, Danielson, & Hallahan, 2002) suggested that three criteria were important: (1) response to instruction, assessed through progress monitoring and evaluations of the integrity of interventions; (2) assessment of low achievement, typically through norm-referenced achievement tests; and (c) application of exclusionary criteria to ensure that low achievement is not due to another disability (e.g., mental retardation, sensory disorder) or to environmental and contextual factors (e.g., limited English proficiency).

IDEA 2004 is consistent with this hybrid model of classification. It explicitly indicates that children may be identified for special education only with documentation that low achievement is not the result of inadequate instruction. In addition, IDEA identifies six domains of low academic achievement in which LDs may occur. It requires assessment of the traditional exclusionary criteria, a process that remains inherently vague when the issues are factors such as emotional difficulties, which may co-exist or result from poor achievement, or economic disadvantage, the role of which in relation to low achievement is difficult to separate from LD in the absence of adequate instruction.

References

Bradley R, Danielson LE, Hallahan DP. Identification of learning disabilities: Research to practice. Mahwah, NJ: Erlbaum; 2002.

Donovan MS, Cross CT National Research Council Committee on Minority Representation in Special Education. Minority students in special and gifted education. Washington, DC: National Academies Press; 2002.

Fletcher JM, Lyon GR, Fuchs LS, Barnes MA. Learning disabilities: From identification to intervention. New York: Guilford; 2007.

Francis DJ, Santi KL, Barr C, Fletcher JM, Varisco A, Foorman BR. Form effects on the estimation of students' oral reading fluency using DIBELS. Journal of School Psychology. in press. [PMC free article]

Graham S, Perin D. Writing next: Effective strategies to improve writing of adolescents in middle and high schools-A report to Carnegie Corporation of New York. Washington, DC: Alliance for Excellent Education; 2007b.

Hanushek EA, Kain JF, Rivkin SG. Does special education raise academic achievement for students with disabilities? Cambridge, MA: National Bureau of Economic Research; 1998. (Working Paper No. 6690)

Jimerson SR, Burns MK, VanDerHeyden AM. Handbook of response to intervention: The science and practice of assessment and intervention. Springfield, IL: Charles E. Springer; 2007.

Mann L. On the trail of process. New York: Grune & Stratton; 1979.

National Association of State Directors of Special Education (NASDSE) Response to intervention: Policy considerations and implementation. Alexandria, VA: Author; 2006.

Reschly DJ, Tilly WD. Reform trends and system design alternatives. In: Reschly D, Tilly W, Grimes J, editors. Special education in transition. Sopris West: Longmont, CO; 1999. pp. 19-48.

Scammacca N, Roberts G, Vaughn S, Edmonds M, Wexler J, Reutebuch CK, et al. Reading interventions for adolescent struggling readers: A metaanalysis with implications for practice. Portsmouth, NH: RMC Research Corporation, Center on Instruction; 2007.

Swanson HL, Hoskyn M, Lee C. Interventions for students with learning disabilities: A meta-analysis of treatment outcome. New York: Guilford; 1999.

U.S. Department of Education. 25th annual report to Congress on the implementation of the Individuals with Disabilities Education Act. Washington, DC: Author; 2007.

Vaughn S, Wanzek J, Fletcher JM. Multiple tiers of intervention: A framework for prevention and identification of students with reading/learning disabilities. In: Taylor BM, Ysseldyke J, editors. Educational interventions for struggling readers. New York: Teacher's College Press; 2007. pp. 173-196.

Vaughn SR, Wanzek J, Woodruff AL, Linan-Thompson S. A three-tier model for preventing reading difficulties and early identification of students with reading disabilities. In: Haager DH, Vaughn S, Klingner JK, editors. Validated reading practices for three tiers of intervention. Baltimore, MD: Brookes; 2006. pp. 11-28.

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Response to Intervention: Preventing and Remediating Academic Difficulties

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Children struggle to learn reading, mathematics, and writing skills for many reasons, including growing up in economically disadvantaged settings, low proficiency in English, emotional difficulties, and even inadequate academic instruction ( Donovan & Cross, 2002). Some children are eventually identified with learning disabilities (LDs), representing approximately 5% of the school-age population and approximately 50% of students identified with disabilities in schools ( U.S.

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According to Nabil (2015), " Teachers should assess the students' needs and abilities in several ways, such as observation and student conferences. In this way, teachers will know more about the students' abilities and needs when they use different approaches, and can then provide appropriate instructions and effective teaching methods for these students" (pg. 67).

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This article explains the necessity of intensive intervention and the use of Data-Based Instruction.

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This article explores the effect that early intervention has on students who are at-risk. Although the majority of the research focuses on college students, portions of the research examine children as well.

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This article gives the effects of direct instruction in Math for students with learning difficulties. The results indicated that direct instruction was effective compared to traditional instruction.

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The Effect of Direct Instruction Strategy on Math Achievement of Primary 4th and 5th Grade Students with Learning Difficulties | Al-Makahleh | International Education Studies

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The Effect of Direct Instruction Strategy on Math Achievement of Primary 4th and 5th Grade Students with Learning Difficulties

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Once students can properly place events on a timeline, they are unable to use critical thinking skills to draw their own conclusions about those placements.
Example: Students may place two events such as the beginning of one civilization and the ending of another civilization at the same date, but are unable to draw the conclusion that the first civilization was taken over by the second civilization.

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Students are unable to place events properly on a timeline.