用YOLO检测课堂行为，发现学生注意力最后阶段明显下降。
Classroom Behavior Monitoring with YOLO An Empirical Study in Higher Education Settings
- 基于YOLOv11分析九类课堂行为，实现自动化监控。
- 实测显示讲座末期学生专注度显著下降。
- 适合教育技术、智能教学系统研究者参考。
课堂行为监测对评估学生参与度和提升教学效果至关重要。传统观察方法主观性强且难以扩展。本研究在越南银行学院收集真实课堂视频，构建了BAV-Classroom数据集，包含九类行为标注。对比评估了前沿计算机视觉模型，其中YOLOv11表现最优。实验结果表明，学生专注度常在讲座末期明显下降，凸显维持参与度的挑战。研究证实了计算机视觉在自动化课堂监控中的可行性，为教学质量管理提供有力支持。
原文摘要 · Abstract (English)
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.
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