用深度学习分析面部表情、眼神和鼠标行为，实时识别在线学生注意力不集中
Detecting Student Disengagement in Online Classes Using Deep Learning: A Review
- 结合视觉与行为数据，用深度学习模型捕捉学生专注度变化
- 38项研究验证了面部表情与鼠标活动对注意力的预测有效性
- 适合教育技术开发者和在线教学研究者参考
在线学习中的学生注意力分散已成为重大挑战，尤其在疫情后更为突出。本文综述了用于检测注意力不集中的深度学习技术，重点探讨计算机视觉与情感计算方法的有效性。分析了近期研究中基于面部表情、眼动追踪和姿势判断学生注意力的成果，以及非面部指标如鼠标活动的应用。通过对38项精选研究的系统回顾，梳理了该领域的关键指标、方法与模型，为未来在线课堂中实时注意力监测研究提供洞见。
原文摘要 · Abstract (English)
Student disengagement in online learning has become a critical challenge, particularly post-pandemic. This review explores deep learning techniques used to detect disengagement, emphasizing computer vision and affective computing as effective approaches. We examine recent studies focusing on facial expressions, eye movements, and posture to assess student attention, along with non-face-based indicators like mouse activity. A systematic review of 38 selected studies outlines the indicators, methods, and models employed in this field, providing insights for future research on real-time engagement monitoring in online classrooms
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