Building Interpretable Descriptors for Student Posture Analysis in a Physical Classroom
No Thumbnail Available
Permanent Link
Author/Creator
Author/Creator ORCID
Date
2021
Type of Work
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.