S6-CT6-1 - Supporting Instructor Reflection on Employed Teaching Techniques via Multimodal Instructor Analytics
1. Innovative Practice Work In Progress1 Central Michigan University
Introduction
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 multimodal
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.