Introduction
One issue that has been a crucial part of special education, even prior to the Individual’s With Disabilities Education Act (IDEA, Originally the Education for All Handicapped Children Act of 1975), is the criteria for special education eligibility in public schools. Currently there is a shift that began with the reauthorization of IDEA, now IDEA-B that recommended, but did not require public schools to use other models for the determination of specific learning disabilities, yet did not prohibit the use of the discrepancy model. I am particularly interested in the eligibility determination of student’s with specific learning disabilities as it comprises the majority of students receiving special education services that is determined by evaluations conducted by school staff (diagnosticians or school psychologists).
History
Going back to 1975, just after the Individuals with Disabilities Education Act (IDEA) was introduced a federally mandated cap for all students aged 5 years to 17 years decreed that only two percent of students should be coded with a specific learning disability (Zirkel, 2013). From 1988 through 1999, the number of students identified with a specific learning disability increased by 41% and accounted for 60% of special education enrollments. In the following decades, 1990-2001, specific learning disabilities accounted for approximately 50% of all students enrolled in special education (Zirkel, 2013). As cited in Zirkel (2013), the National Center for Learning Disabilities reported a slight decline in the number of student aged 6-21 with specific learning disabilities. This report states that most likely factors for the decline in students identified with specific learning disabilities enrollments were the emergence of the tiered Response to Intervention programs (RTI) and increased litigation. As a note, the prevailing model for specific learning disability at the time was the use of a simple discrepancy model of identification. The model used the student’s full-scale intelligence source and compared achievement scores. If any of the student’s achievement scores were greater than 15 points below the full-scale intelligence score (one standard deviation), then the student was eligible for special education services. Many states and schools districts continue to utilize the sever discrepancy model that requires a thirty (30) point discrepancy between IQ and achievement scores for identification of students with specific learning disabilities.
Updating the Eligibility Criteria
Callinan, Theiler, and Cunningham (2015)* present an argument for the need of a “double deficit theory (p. 272) that includes addressing the cognitive processing deficit directly coupled with the aptitude (achievement)-intelligence discrepancy model. This theory suggests that naming speed and phonological processing are key indicators of a learning disability as well stating that verbal processing is related to learning disabilities (Callinan, Theiler, & Cunningham, 2015). The results did show an increased rate of identification of students with specific learning disabilities, 77%, when compared to the severe discrepancy model, 56% (Callinan, Theiler, & Cunnigham, 2015, p. 277).
Another model, the process-deficit model, or patterns of strengths and weaknesses model has come to be one of the available models used in public schools for identification of student’s with specific learning disabilities (Taylor, Miciak, Fletcher, & Francis, 2017)*. This model uses the results of intelligence tests with that of achievement (aptitude) tests to determine if a pattern of strengths and weaknesses presents through the correlation of areas of intelligence with that of aptitude. The study by Taylor, Miciak, Fletcher, & Francis, (2017), sought to determine if there was a significant discrepancy in the identification of students using a cognitive deficit model when using different tests. The results showed that there out of 1000 students, 13 student were identified by both batteries of tests used, however, 29 students were identified when using different achievement tests (Taylor, Miciak, Fletcher, & Francis, 2017, p. 454).
In the evaluation and identification of student’s with an emotional disturbance, standard practice to first evaluate the student’s cognitive ability and aptitude. Over the past 40 years since special education has been mandated by federal law in public schools, federal criteria for determination of student’s with specific learning disabilities has been revised several times to adjust to current practices while the criteria for emotional disturbance has remained largely unchanged since 1975 (Hanchon & Allen, 2018)*. As a result, most states, and most school districts have developed policies and procedures on the evaluation process of emotional disturbance, Hanchon and Allen (2017) noted: “The results of this study suggested that although respondents generally found value in many commonly recommended sources of data (e.g., behavior rating scales, third-party interviews), their reported collection of such data lacked consistency for the purpose of informing eligibility determination. Notably, classroom observations, as well as parent, teacher, and student interviews, were reported to be inconsistently included (p. 178).”
Conclusion
The common theme from each of the articles is that there is no standard method of special education evaluation and determination of eligibility. I included the article on emotional disturbance as there is an abundance of literature and studies on learning disability identification as it is the most predominate disability in special education in the United States, yet the identification of emotional disturbance relies partially on the results of an evaluation for learning disabilities. At this point I agree more with the study by Taylor, Miciak, Fletcher, & Francis, (2017), as it sought to discover a better method of evaluation. The federal law allows for evaluation through a discrepancy model, a process-deficit model or the use of an RTI model (Response to Intervention) or instructional model. Through these articles, which I know is another topic, there remains of lack of research or recommendations on the interpretation of the results in guiding instruction once the student is identified as eligible.
Outcomes of Special Education Students
As with researching the eligibility factor of special education, I also wanted to target some outcomes of students with special education and see if there are links to factors that predict over-identification of subgroups of populations. I found that socio-economic status and minority status tend to link to increased special education referral and disciplinary actions, which in turn, increased disciplinary actions lead to increases in special education referrals. Here I choose the outcome of retention rates and graduation rates as influenced by special education status and disciplinary rates.
According to a study in 1990-91 by Barret and Newman (2018)*, 71.6% of students were retained prior to being evaluated for special education as compared to the national retention rate of just 11%. Students identified with a specific learning disability had a retention rate of 55% by the 8th grade and were twice as likely to be retained as compared to general education students. In this study consisting of 344 special education students in grades K-12, 229 students had been retained prior to a special education evaluation with 33.2% never having been retained, 44.8 retained once, 17.5% retained twice, and .9% retained three times prior to their initial special education evaluation. A meta-analysis conducted by Reschly and Christenson (2013) suggested that grade retention and social promotion had a negative effect on students’ academic and social-emotional functioning. Grade retention is also one of the most powerful predictors of high school dropouts, having a likelihood 11 times higher than students that had not been retained (Reschly & Christenson, 2013). If special education services are effective, then the retention rates of students with specific learning disabilities should be similar to students in the general education population (Morgan, Frisco, Farkas and Hibel, 2010).
Along with retention rates being a predictor of students not graduating from high school, school suspensions are also linked to poor academic performance and high school completion rates. Within the public school system, there continues to be evidence suggesting that students receiving special education services receive in-school and out-of-school suspension at a disproportionality higher rate than their peers (Richard and Hardin, 2018)*. A link also exists between the socio-economic status, minority status and gender and increased disciplinary rates. According to Richard and Hardin (2018), students in these marginalized groups are more likely to be suspended than those students not in these groups. Within these groups, Loe and Feldman (2007) suggests that students diagnosed with ADHD can lead to poor grades, lower standardized test scores, and increased retention rates. These indicators can lead to special education referrals and evaluations of student with ADHD for learning disabilities instead of providing remedies or interventions specific to characteristics of ADHD (Loe & Feldman, 2007).
It is not only a racial or income inequity that faces many of the students in public schools, it is the realization that students in these subgroups also face an increased likelihood of being identified as a student with a disability and being coded (or labeled) as a special education student (Ford & Russo, 2016)*. The evidence provide in Ford and Russo’s (2016) study showed that black students are two times more likely to be referred and identified as a student with a disability through special education. Black students, once identified as special education students, are also more likely to be educated in a more restrictive environment that is separate from their non-disabled peers (Ford & Russo, 2016). Along with these findings is that many people in a lower socio-economic status tend to cluster in certain areas and evidence shows that there are disparities based on race, socio-economic status and geographic location (Baker & Ramsey, 2010).
It seems there is an interconnected and spiraling effect that occurs with retention rates increasing based on special education status and disciplinary actions taken. I did not have a stand out article in this section since the articles are very connected. The Barrett & Newman (2018) article held a little favor for me as in provided the research as a guiding tool to use to prevent the unnecessary retention of student. This is the thinking that we should be moving away from a wait to fail model and provide students with better instructional methods and interventions prior to special education referral and prior to considering retention. Again, none of the articles seemed to offer much in the way of what to do from their findings, however, that is another topic in itself.
References:
Baker, B. D., & Ramsey, M. J. (2010). What we don’t know can’t hurt us? Equity consequences of financing special education on the untested assumption of uniform needs. Journal of Education Finance, 35(3).
Barrett, C., & Newman, D. (2018). Examining MTSS implementation across systems for SLD identification: a case study. School Psychology Forum, 12(1), 30–43.
Callinan, S., Theiler, S., & Cunningham, E. (2015). Identifying learning disabilities through a cognitive deficit framework: Can verbal memory deficits explain similarities between learning disabled and low achieving students? Journal of Learning Disabilities, 48(3), 271-280.
Ford, D. Y., & Russo, C. J. (2016). Historical and legal overview of special education overrepresentation: access and equity denied. Multiple Voices for Ethnically Diverse Exceptional Learners, 16(1), 50-57.
Fuchs, D., Fuchs, L., & Stecker, P. (2010). The “blurring” of special education in a new continuum of general education placements and services. Exceptional Children, 76(3), 301–323.
Hanchon, T., & Allen, R. (2017). The identification of students with emotional disturbance: Moving the field toward responsible assessment practices. Psychology in the Schools, 55, 176-189. Wiley Periodicals.
Loe, I. M., Feldman, H. M., (2007). Academic and educational outcomes of children with ADHD, Journal of Pediatric Psychology, 32(6), 643–654.
Morgan, P., Frisco, M., Farkas, G., & Hibel, J. (2010). A propensity score matching analysis of the effects of special education services. Journal of Special Education, 43(4), 236–254.
Reschly, A., & Christenson, S. (2013). Grade retention: historical perspectives and new research. Journal of School Psychology, 51(3), 319-322.
Taylor, W. P., Miciak, J., Fletcher, J., & Francis, D. (2017) Cognitive discrepancy models for specific learning disabilities identification: Simulations of psychometric limitations. Psychological Assessment, 29(4), 446-457. American Psychological Association.
Zirkel, P. (2013). The trend in SLD enrollments and the role of RTI. Journal of Learning Disabilities, 46(5), 473–479.