StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking

TL;DR

StuArt is an automatic classroom observation system that recognizes student behaviors related to engagement, tracks their trends, and visualizes data to assist instructors in providing individualized guidance while preserving student privacy.

Contribution

The paper introduces StuArt, a novel system that automatically recognizes and tracks student behaviors related to engagement, with privacy-preserving features and user-friendly visualization for classroom monitoring.

Findings

Demonstrated robustness on real classroom videos

Effectively recognizes five key student behaviors

Provides useful visualizations for instructor insights

Abstract

Each student matters, but it is hardly for instructors to observe all the students during the courses and provide helps to the needed ones immediately. In this paper, we present StuArt, a novel automatic system designed for the individualized classroom observation, which empowers instructors to concern the learning status of each student. StuArt can recognize five representative student behaviors (hand-raising, standing, sleeping, yawning, and smiling) that are highly related to the engagement and track their variation trends during the course. To protect the privacy of students, all the variation trends are indexed by the seat numbers without any personal identification information. Furthermore, StuArt adopts various user-friendly visualization designs to help instructors quickly understand the individual and whole learning status. Experimental results on real classroom videos have demonstrated the superiority and robustness of the embedded algorithms. We expect our system promoting the development of large-scale individualized guidance of students. More information is in https://github.com/hnuzhy/StuArt.