TY - GEN
T1 - Supporting Instructor Reflection on Employed Teaching Techniques via Multimodal Instructor Analytics
AU - Eickholt, Jesse
N1 - Funding Information:
This material is based upon work supported by Google Cloud.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - 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.
AB - 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.
KW - critically reflective teaching
KW - instructor analytics
KW - instructor monitoring
KW - learning analytics
KW - multimodal learning analytics
UR - http://www.scopus.com/inward/record.url?scp=85098591475&partnerID=8YFLogxK
U2 - 10.1109/FIE44824.2020.9273968
DO - 10.1109/FIE44824.2020.9273968
M3 - Conference contribution
AN - SCOPUS:85098591475
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 2020 IEEE Frontiers in Education Conference, FIE 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 October 2020 through 24 October 2020
ER -