Detecting Student Disengagement in Online Classes Using Deep Learning: A Review
It addresses the challenge of monitoring student engagement in online learning, particularly post-pandemic, but is incremental as it synthesizes existing research without introducing new methods.
This review tackles the problem of detecting student disengagement in online classes by exploring deep learning techniques, such as computer vision and affective computing, based on a systematic analysis of 38 studies.
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