Supporting Instructor Reflection on Employed Teaching Techniques via Multimodal Instructor Analytics

摘要

This work-in-progress in the Innovative Practice Category describes the use of multimodal data capture to inform instructors’ awareness of their activities in the classroom. Broadly construed, learning analytics is the collection and analysis of data in an educational context with the aim of improving educational outcomes. To capture a more wholistic characterization of an educational context, there has been increased interest in multimodal data such audio, gestures, positioning and movement. These data can characterize the content delivered and teaching techniques employed by the instructor. Instructor reflection on both may lead to improvements in instruction.Presented here is IATracer, a lightweight system for multi-modal instructor data capture consisting of a lavalier microphone paired with a positioning badge. The microphone captures classroom audio and using Google Cloud’s Speech-to-Text API with diarization, the instructor’s speech can be isolated and transcribed. Analysis of this text can provide insights into what topics were covered, for how long and what questions were asked. Additional analysis could provide the instructor feedback on the delivery (e.g., long monologues) and the level of student interaction (e.g., dialogue, questions directed towards students). Novel aspects of this work-in-progress include the lightweight, economical nature of the system and its use of Google Cloud services. The insights generated by the system will enable faculty to reflect upon their employed teaching techniques and the content of their interaction with students. Such reflection ensures alignment of employed technique with intent.