[3Win5] Poster session 3

[3Win5-73] Prototyping and Evaluation of a Participant Behavior Recognition System in Online Classes

〇Yulai Zhang¹, Yimeng Sun¹, Miki Ueno¹, Takao Nakaguchi¹, Masaharu Imai¹ (1.The Kyoto College of Graduate Studies for Informatics)

Keywords:online education, educational support, learning state estimation, gaze estimation

Online education has rapidly advanced, offering recognized convenience and flexibility. However, effective methods to monitor student concentration remain a challenge. This study develops a feedback system combining gaze tracking and head movement recognition to enhance online class quality and teacher-student interaction. A 30-minute simulated online class with five participants acting as "focused" and "distracted" students was conducted via Zoom. Results showed "focused" students had lower gaze-away rates, while "distracted" students had higher rates, aligning with their roles. The system demonstrated its ability to accurately evaluate concentration and its potential to improve online education. Future work will focus on broader validation and system refinement.