StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking
This addresses the need for individualized student observation in classrooms to improve engagement and guidance, though it is an incremental application of existing computer vision methods to a new educational domain.
The paper tackles the problem of instructors being unable to observe all students individually during classes by presenting StuArt, an automatic system that recognizes five student behaviors (hand-raising, standing, sleeping, yawning, and smiling) and tracks their trends, with experimental results showing superiority and robustness on real classroom videos.
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.