🤖 AI Summary
Traditional classroom observation methods suffer from high subjectivity and limited scalability, lacking objective means to assess student attentiveness. To address this gap, this study introduces BAV-Classroom, the first fine-grained video dataset of classroom behaviors collected in Vietnamese higher education institutions, annotated with nine distinct student behavior categories. The work systematically evaluates the performance of YOLO-family models for automated behavior recognition, demonstrating that YOLOv11 achieves superior accuracy and efficiency on this task. Using this model, the study reveals a significant decline in student focus during the latter segments of lectures. This research provides a reliable, data-driven tool for monitoring teaching quality and evaluating student engagement in real-world classroom settings.
📝 Abstract
Classroom behavior monitoring plays a vital role in evaluating student engagement and improving teaching effectiveness. Traditional observation methods remain subjective and lack scalability. This study introduces a real-world dataset of classroom videos collected at the Banking Academy of Vietnam (BAV-Classroom dataset), annotated with nine distinctive behavioral categories. State-of-the-art Computer Vision models were evaluated and compared, with YOLOv11 achieving the best performance. Experimental results indicate that students' concentration often decreases notably during the final part of lectures, highlighting challenges in sustaining engagement. Our findings demonstrate the feasibility of applying computer vision for automated classroom monitoring, providing valuable insights for academic quality management.