Multimodal Teaching Analytics

The new wave of technological innovations enables combining data from multiple sources and with different modalities to analyze learning and teaching in classrooms. Our research uses multimodal methods and classroom sensing technologies to track engagement, learning, and teaching in technology-enhanced STEM classrooms.


An Integrated Faculty Professional Development Model Using Classroom Sensing and Machine Learning to Promote Active Learning in Engineering Classrooms

The TEACHActive professional development model uses classroom sensing and machine learning to promote active learning in engineering classrooms. The data collected as part of integration of this model is expected to promote systematic improvement in evidence-based teaching, which will positively impact student engagement in classrooms.

For more information about this project, check out the TEACHActive website.

This research is supported in part by NSF award #2021118. The opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.