Abstract

Faculty professional development is known to be a key factor contributing to the effective implementation of evidence-based teaching in STEM classrooms. In this research, we developed TEACHActive, an innovative classroom analytics-driven professional development model that supports the reflective practices of engineering instructors in higher education. TEACHActive uses machine learning techniques within a camera-based classroom sensing system that tracks behavioral features of interest in classrooms. Following design-based implementation research, we rapidly enacted, tested, and revised the TEACHActive model with engineering instructors. This study reports the results of the first iteration completed in the spring semester of 2021. Specifically, we examined the TEACHActive implementation and deployment in engineering classrooms with the analysis of instructors’ perceived successes and challenges. The paper presents implications for using the classroom analytics-driven professional development with educators in higher education.

Citation

Baran, E., AlZoubi, D. & Karabulut-Ilgu, A. (2022). Leveraging Engineering Instructors’ Professional Development with Classroom Analytics. In E. Langran (Ed.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 1769-1775). San Diego, CA, United States: Association for the Advancement of Computing in Education (AACE). Retrieved November 29, 2023 from https://www.learntechlib.org/primary/p/220948/.

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