Detecting Student Disengagement in Online Classes Using Deep Learning: A Review

TL;DR

This review analyzes deep learning methods for detecting student disengagement in online classes, focusing on facial cues, eye movements, posture, and non-face indicators to improve real-time engagement monitoring.

Contribution

It systematically reviews 38 studies on deep learning techniques for engagement detection, highlighting effective computer vision and affective computing approaches.

Findings

Facial expressions and eye movements are key indicators.

Deep learning models effectively classify engagement levels.

Non-face cues like mouse activity also contribute to detection.

Abstract

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