The Special Education Field

The Unique and Specialized Nature of The Special Education Field
In an ideal world, the general education classroom teacher would be able to differentiate instruction to allow all students to succeed. However, considering realistic limitations and constraints of teacher efficacy, time, and resources, some researchers have been skeptical of what can realistically be implemented with valid and reliable results. (Fuchs & Fuchs, 2015; McMaster & Fuchs 2002; O’Connor & Jenkins, 1996). Although there are various general education approaches designed to scaffold students of varied achievement levels, including cooperative learning (CL) and peer-assisted learning strategies (PALS), they have not been proven to reliably help students with special needs
(Fuchs, D., & Fuchs, L.S. (2005); McMaster & Fuchs, 2002; O’Connor & Jenkins, 1996). There are particular students with distinct needs that cannot be met in the general education classroom. It is for this population that special education instruction must be specifically designed, implemented and revised for improvement based on individual progress.
Research supports the argument that certain students with special education labels display categorical differences in both academic achievement and observable behaviors (Birch, 1964; Caffrey & Fuchs, 2007; Torgerson 1982; Ysseldyke 1982). Therefore, it stands to reason that they would react differently to specific academic interventions, when compared to typically achieving peers (Caffrey & Fuchs, 2007; Deno, 1990). Students have unique needs and have varying levels of responsiveness to different methods of instruction (Deno, 1990). Students who struggle with memory, task orientation, distractibility, and impulse control need specific, targeted support to succeed in intellectual tasks that require these self-organizing cognitive functions (Birch, 1964; Torgerson, 1982). Educators have the ethical and legal responsibility to ensure that all children, including those with special education needs, are ensured a free and appropriate public education. The right to an appropriate education entitles students to the individualized and specialized support they require to facilitate their success (IDEA, 2004). However, the size and heterogenous nature of most American general education classrooms make the design and implementation of targeted interventions an arduous task. If the challenge of an inclusion model is undertaken, the teachers’ workload increases exponentially, and there is potential for decreased fidelity and efficiency of interventions.
Critics of the inclusion trend highlight the complexity of designing an effective CL or inclusion environment. Despite good intentions, attempts at inclusive practices such as CL and PALS have not proved reliably effective for supporting all students in achieving rising state and national standards (Fuchs Lecture, 2/13/2017, Fuchs and Fuchs, 2015; Fuchs et al., 2012; McMaster & Fuchs 2002; O’Connor & Jenkins, 1996; Powell & Stecker, 2014; ). Although there has been research to support the benefits of both PALS and CL, there has been no evidence to suggest it is effective for all students. (Fuchs Lecture 3/27/2017) Proponents of special education raise concerns about whether these inclusive strategies are the most effective approaches to close the achievement gap between low achievers and their peers (McMaster & Fuchs 2002; Deno, 2014). Alternatively, there are evidence-based strategies that have been designed to allow teachers to continuously monitor student progress and redesign as needed according to achievement data in a manageable and systematic way (Deno, 2014). Data-based individualization, and curriculum-based measures are two iterative processes that, when implemented effectively, allow for the student to receive frequent, consistent, systematic and individualized instruction (Powell & Stecker, 2014; Deno, 2014; Lemons, Kearns & Davidson, 2014).
However, an important caveat to the effectiveness of these strategies is the frequency and fidelity of their implementation (Deno, 2014; Lemons, Kearns & Davidson, 2014).
As the trend of inclusion blurs the lines between special and general education, DBI and similar strategies are not being used as originally intended (Deno, 2014).The reiterative nature of DBI that allows it to be so intensive and individualized to student needs is arguably only possible in a special education setting. DBI and CBM were designed to be implemented frequently and with fidelity by specifically-trained special educators. It seems unrealistic to expect a general educator to conduct, assess, and redesign such intensive evaluations at the rate that these interventions require to be most effective, while also balancing the needs of twenty-plus other students. We must consider these constraints in our efforts to ensure that all students are receiving the appropriate education supports that they need to
succeed.
Although there may be some benefits to inclusive practices such as CL and PALS, for typically achieving students, there is not enough research to support the successful implementation of these strategies to allow students with learning disabilities to succeed. Heterogeneity provides an obstacle to ensuring effective implementation of DBI due to its intensive and reiterative nature (Deno, 2014; McMaster & Fuchs 2002; O’Connor & Jenkins, 1996). This concern highlights the importance of special education’s recognition as a unique and specialized entity. Certain students need special services, there are no proven support strategies proven reliably effective for all students in general education classrooms. Therefore, in order to appropriately serve students, special education must be a separate, specialized field.

References
Birch, H.G.
(1964). The problem of brain damage in children. In H.G. Birch (Ed.), Brain damage in children: Biological and social aspects. New York: Williams and Wilkins.

Caffrey, E., & Fuchs, D. (2007). Differences in performance between students with learning disabilities and mild mental retardation: Implications for categorical instruction. Learning Disabilities Research and Practice, 22, 119-128. Deno, S.L. (1990). Individual differences and individual difference: The essential difference of special education. The Journal of Special Education, 24(2), 160-173. Deno, S.L. (2014). Reflections on progress monitoring and data-based intervention. Advances in Learning and Behavioral Disabilities, 27, 171-194. Emerald Group Publishing.

Fuchs, D., & Fuchs, L.S. (2005). Peer-Assisted Learning Strategies: Promoting word recognition, fluency, and reading comprehension in young children. The Journal of Special Education, 39, 34-44.

Fuchs, D. & Fuchs, L.S. (2015). Rethinking service delivery for students with significant learning problems: Developing and implementing intensive instruction. Remedial and Special Education, 36(2),
105-111.

Lemons, C.J., Kearns, D.M., & Davidson, K.A. (2014). Data-based individualization: Intensifying interventions for students with significant reading disabilities. Teaching Exceptional Children, 46(4), 20-29. McMaster, K.N., & Fuchs, D. (2002). Effects of cooperative learning on the academic achievement of students with learning disabilities: An update of Tateyama-Sniezek’s review. Learning Disabilities Research and Practice, 17(4), 107-117. O’Connor, R.E., & Jenkins, J.R. (1996). Cooperative learning as an inclusion strategy: A closer look. Exceptionality, 6, 29-51. Powell, S.R., & Stecker, P.M. (2014). Using data-based individualization to intensify mathematics intervention for students with disabilities. Teaching Exceptional Children, 46(4), 31-37.
Ysseldyke, J. E., & Algozzine, B. (1983). LD or Not LD That's Not the Question!. Journal of Learning Disabilities, 16(1), 29-31.