Bridging the Gap: Computational Thinking as a Pathway to Equitable Math Education for Neurodiverse Learners

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Abstract

Students who receive special services in mathematics or neurodiverse learners (NDLs) deserve to learn mathematics at a rigorous and equitable level as their peers. Yet, many of these students are not reaching proficiency. Computational thinking (CT) may function as a bridge between students' mathematical learning and their everyday lives. We argue that computational thinking is a pathway for NDLs to follow that will bridge the connections between math instruction and the real-life problems with which NDLs are eager to engage. We identified the critical elements needed to support rural special educators' integration of CT into math instruction by identifying intersections between CT, Math, and the assets of NDLs, as well as barriers to implementation. By leveraging connections from an existing RPP (Research-Practice Partnership), which focuses on CS education and Business intersection in rural Maine schools, we created an opportunity for participatory research for special educators and paraprofessionals often overlooked in these processes. We employed a multi-method, design-based research (DBR) approach. Through observational, survey, and interview data, we found that 1) educators are not currently receiving support for integrating CT in special education classrooms, 2) activities piloted by our educators were well received by students, 3) current standards and Individualized Educational Plan (IEP) requirements for NDLs are a significant barrier to implementation. The need for expanded, regular PD for special educators is evident, as is the potential for CT as a bridge for mathematical understanding for NDLs.

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APA

Kastelein, K., Dawson, K., & Tate, R. (2024). Bridging the Gap: Computational Thinking as a Pathway to Equitable Math Education for Neurodiverse Learners. In SIGCSE 2024 - Proceedings of the 55th ACM Technical Symposium on Computer Science Education (Vol. 2, pp. 1698–1699). Association for Computing Machinery, Inc. https://doi.org/10.1145/3626253.3635476

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