DEVELOPMENT, VALIDATION, AND IMPLEMENTATION OF A MAINSTREAMING PROCESS TO TRANSITION STUDENTS FROM SELF-CONTAINED SPECIAL EDUCATION INTO GENERAL EDUCATION PLACEMENTS

Michael Ryan Hunsaker

Abstract


We developed a set of computational tools specifically to guide qualified special education students back into general education. These tools include a decision tree to identify candidate students and elucidate successful placement in general education. Candidate students enter a process involving selection of general education classroom, data collection, and finally how to make the final transition out of special education self-contained placements. In the 2015-2016, we undertook a limited implementation of these transenvironmental programming tools and facilitated the transition of 10 of 20 identified candidate students from self-contained academic special education classrooms into general education placements. In the 2016-2017 school year, we extended this process to include 4 schools. 16 of 53 identified candidate students from self-contained academic special education classrooms were able to transition into general education placements. In an extension of the model district-wide, 9 of 26 identified students from behavior/SEL unit classrooms, and 9 of 9 identified students from Life Skills/SID unit classrooms were successfully transitioned into a general education with part-time special education placement. A high percentage of the remaining candidates received >50% of their day in general education classrooms and/or were placed in less restrictive self-contained classrooms. Overall, 54% of identified candidate students were able to access a less restrictive environment as defined by IDEIA. Further, computational analyses using regression tree, unbiased hierarchal clustering, and support vector machine methods are presented to demonstrate the robustness of these methods by recapitulating the results using solely data from special education evaluations.

 

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transenvironmental programming, psychoeducational evaluation, MTSS, inclusion, mainstreaming, machine learning

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References


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