An Integrated Faculty Professional Development Model Using Classroom Sensing and Machine Learning to Promote Active Learning in Engineering Classrooms [2020-23]

Baran, E. (PI), Gilbert, S. (Co-PI), Karabulut-Ilgu, A. (Co-PI),  & Jiang, S. (Co-PI). An Integrated Faculty Professional Development Model Using Classroom Sensing and Machine Learning to Promote Active Learning in Engineering Classrooms. National Science Foundation (Total award: $299,879), 2020-23

Summary: Faculty professional development is known to be a key factor contributing to evidence-based teaching in STEM classrooms. Faculty need opportunities for frequent observation, feedback, and reflection on the use of their active learning strategies in their classrooms. This research aims to establish an integrated faculty professional development model (TEACHActive) through an automated classroom observation system and a feedback dashboard on the in-class implementation of various active learning strategies in engineering classrooms. TEACHActive integration will include three consecutive components: (1) Active learning training, (2) four-week automated classroom observation and (3) feedback and reflection.  The research team will identify how instructors’ active learning adoption progresses with the TEACHActive model by measuring longitudinal changes in instructors’ teaching beliefs, facilitation strategies, and reflective practices through quantitative and qualitative data. TEACHActive offers a novel approach to analyzing automated classroom sensing data and embeds that cyber-learning innovation within a theoretically grounded and evidence-based professional development framework.

This project will contribute to the acceleration of engineering educators’ adoption and effective implementation of active learning strategies in engineering classrooms, which will positively influence student engagement. The results will have a broader impact across different majors, levels (undergraduate and graduate student training), campuses, and disciplines beyond STEM. The results will also inform inclusive teaching practices in engineering classrooms by promoting multiple modes of engagement and creating motivating learning environments that encourage the success and persistence of all students.