Deep Learning-Driven Emotion Recognition for Personalized and Adaptive E-Learning Systems

This chapter explores how deep learning–based emotion recognition enhances student engagement and cognitive growth in e-learning environments. By leveraging CNN and LSTM architectures, the proposed framework detects learners' emotions from facial and behavioral cues in real time, enabling adaptive instructional responses. Through camera-based, non-invasive monitoring, emotions such as confusion, curiosity, or satisfaction are quantified to personalize learning experiences and improve retention. The chapter also discusses dataset selection, model design, and integration into Learning Management Systems (LMS). Case insights demonstrate how emotion-aware systems foster empathy, motivation, and inclusivity in digital education. Ethical considerations privacy, fairness, and explainability are addressed to ensure responsible AI deployment. The study establishes emotion recognition as a cornerstone for intelligent, adaptive, and human-centered e-learning ecosystems that align technology with emotional intelligence and pedagogical effectiveness.

Hali tarjima qilinmagan