We developed a set of computational tools specifically to guide 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 7-step process. In 2015-2016, we undertook a pilot implementation of these tools and facilitated the transition of 10 of 20 students from self-contained academic special education classrooms into general education placements. In 2016-2017, we extended this process to include 4 schools. 16 of 53 students from self-contained academic special education classrooms transitioned into general education placements. Extending the model district-wide, 9 of 26 students from behavior/SEL unit classrooms, and 9 of 9 students from Life Skills/SID classrooms transitioned into a general 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.