Deep Learning–Based Emotion Recognition for Monitoring Mental Health in E-Learning Platforms

Abstract

The intensive development of e-learning has heightened issues surrounding the emotional stability and mental stability of the learners, since there is less interaction between the teacher and the learner, the teacher is unable to detect the affective responses that may be signs of distress, disengagement, and anxiety. Reactionary to this, deep learning-based emotion recognition has also become a potential solution to ongoing tracking of the emotional condition of learners and assisting mental health in an online educational setting. This review is a critical investigation of peer-reviewed literature published within the last five years on deep learning techniques of emotion recognition and how they can be used in mental health monitoring on e-learning platforms in institutions of higher learning, at K-12, and corporate learning scenarios. The review summarizes the progress of convolutional neural networks, recurrent networks, transformer models and multimodal learning models, which combine facial expressions, speech, text, and physiological cues. In addition to the technical performance, the review assesses the use of these systems to deduce engagement, stress, anxiety, and depressive tendencies and how the insights can inform adaptive instruction, early intervention, and learner support. Although recent research findings have shown significant improvements in the level of recognition and real-time viability, there are still critical issues concerning the generalizability, bias, interpretability, privacy, and ethical implementation. The trends mentioned in the review include multimodal fusion, explainable and privacy-preserving learning, and longitudinal affect tracking, with a focus on human-centered design and interdisciplinary collaboration. In general, the discussion indicates that emotion recognition based on deep learning could be used responsibly and beneficially in the context of mental health awareness in e-learning to act as a supplementary component and enhancement of human judgment and care, but not a substitute.