Classroom Analytics for the Teacher, by the Teacher: Building Interpretable Descriptors for Student Posture Analysis in a Physical Classroom
Lujie Chen, David Gerritsen
Jun 30, 2021 20:40 UTC+2
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Session PS1
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Gather Town
Jul 02, 2021 14:10 UTC+2
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Session PS2
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Gather Town
Keywords: classroom analytics, posture analysis, student activity recognition, multimodal learning analytics
Abstract:
This research presents a process for simplifying video labeling and feature generation when building classification systems from real classrooms. Using video from a single, wide-angle recording of a live classroom, we create a low-level feature set of posture primitives built on keypoints from OpenPose. We use that feature set to build a posture recognition model of "natural labels" built from a scripted posture video using the same classroom. This model provides automatic labels for the real classroom data. We then derive a set of interpretable descriptors to characterize student-specific posture pattern dynamics. We show that those descriptors are able to discriminate between subtle differences in learning activities in a real college classroom.