Utilizing Machine Learning for Behavioral Analysis in Educational Environments: A Study on Student Engagement and Classroom

Abstract

The quality of students’ learning experiences is closely linked to their level of interest, effective teaching practices, and a safe, supportive classroom environment. While skilled teachers can simplify challenging subjects and help students achieve academic success, learning outcomes remain limited when students are disengaged or unmotivated. For school administrators and education policymakers, understanding these classroom dynamics is essential, yet continuous observation of students and teachers is difficult, time‑consuming, and often subjective. Students’ facial expressions and body language offer important clues about their attention, emotions, and engagement. However, it is not realistic for teachers to consistently monitor these subtle cues while teaching. To address this challenge, this concept paper explores the use of machine learning to support behavioral analysis in both physical and online classroom settings. The proposed approach combines YOLO version 7 (You Only Look Once) to detect behaviors such as posture, gestures, and facial presence, with the Facial Action Coding System (FACS) to identify micro‑expressions associated with emotions including happiness, confusion, anger, and boredom. Data are drawn from CCTV recordings in physical classrooms and video recordings from online learning environments. As a concept paper, the empirical results are still in development. Nevertheless, the proposed framework is expected to offer teachers and school administrators clearer, more objective insights into student behavior. These insights may help schools improve learning environments, refine instructional strategies, and better understand whether student disengagement stems from digital device use, teaching methods, or unengaging learning materials.