Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning

Published in CHI 2020 Conference, 2020

Recommended citation: Jensen, E., Dale, M., Donnelly, P., Stone, C., Kelly, S., Godley, A., & D'Mello, S. K. (2020). "Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning." Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2020).

Abstract: Like anyone, teachers need feedback to improve. Due to the high cost of human classroom observation, teachers receive infrequent feedback which is often more focused on evaluating performance than on improving practice. To address this critical barrier to teacher learning, we aim to provide teachers with detailed and actionable automated feedback. Towards this end, we developed an approach that enables teachers to easily record high-quality audio from their classes. Using this approach, teachers recorded 142 classroom sessions, of which 127 (89%) were usable. Next, we used speech recognition and machine learning to develop teacher-generalizable computer-scored estimates of key dimensions of teacher discourse. We found that automated models were moderately accurate when compared to human coders and that speech recognition errors did not influence performance. We conclude that authentic teacher discourse can be recorded and analyzed for automatic feedback. Our next step is to incorporate the automatic models into an interactive visualization tool that will provide teachers with objective feedback on the quality of their discourse.

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This paper was supposed to include a talk at the conference in April 2020, but was cancelled due to the COVID-19 pandemic.