Building Interpretable Descriptors for Student Posture Analysis in a Physical Classroom

Author/Creator ORCID

Date

2021

Department

Program

Citation of Original Publication

Chen, Lujie Karen; Gerritsen, David; Building Interpretable Descriptors for Student Posture Analysis in a Physical Classroom; Educatonal Data Mining 2021; https://educationaldatamining.org/EDM2021/virtual/static/pdf/EDM21_paper_26.pdf

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

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 lowlevel 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.