ARCHIVES

The proliferation of online learning platforms has redefined modern education, offering flexibility and accessibility to students worldwide. However, the lack of direct instructor-student interaction poses significant challenges in monitoring student engagement and emotional well-being, which are critical factors influencing learning outcomes. This research proposes an Emotion Detection System for Online Learning, designed to automatically recognize students’ emotions in real time using facial expression analysis, voice tone detection, and behavioral interaction patterns. By employing advanced machine learning algorithms such as Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), combined with data preprocessing and feature extraction techniques, the system achieves high accuracy in identifying key emotional states including happiness, sadness, anger, surprise, and neutral moods. Experimental results demonstrate the effectiveness of the proposed methodology, highlighting its potential to transform conventional online education into a more interactive, responsive, and emotionally aware learning environment. This study underscores the significance of emotion-aware technologies as a critical component for the next generation of intelligent educational systems.