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
@article{arxiv.2411.10464,
title = {Detecting Student Disengagement in Online Classes Using Deep Learning: A Review},
author = {Ahmed Mohamed and Mostafa Ali and Shahd Ahmed and Nouran Hani and Mohammed Hisham and Meram Mahmoud},
journal= {arXiv preprint arXiv:2411.10464},
year = {2024}
}