MACHINE LEARNING FOR MONITORING STUDENT ENGAGEMENT IN ONLINE CLASSES

One of the biggest challenges facing educational institutions is ensuring student interest in online courses. In virtual learning environments, traditional engagement measurement techniques like teacher observations and self-reported questionnaires are frequently arbitrary and ineffectual. This study introduces a Machine Learning (ML)-based method for tracking student participation in online courses through the analysis of physiological and behavioral indicators such eye movements, facial expressions, audio participation, and interaction patterns. The system uses computer vision, natural language processing (NLP), and deep learning algorithms to deliver real-time assessments of students' levels of involvement and attentiveness. To evaluate engagement thoroughly, the suggested model combines voice activity detection, eye-tracking techniques, sentiment analysis of text-based interactions, and facial identification using Convolutional Neural Networks (CNNs). The system employs supervised learning models such as Support Vector Machines (SVM), Random Forest, and deep learning architectures to classify students into different engagement levels. Additionally, the framework ensures scalability and real-time processing, making it suitable for large-scale virtual classrooms. Real-time monitoring, adaptive feedback systems, and multimodal engagement detection are important components of this strategy that together improve the efficacy of online learning. To guarantee appropriate implementation, ethical issues are also included, such as data privacy, security, and bias mitigation. This study intends to close the gap between inperson and virtual learning settings by automating engagement measurement and giving teachers useful information to increase student participation. The results imply that by facilitating tailored interventions and improving learning outcomes, ML-driven engagement monitoring systems can greatly improve online learning. Future research will concentrate on improving real-time processing, extending multimodal data integration, andinvestigating federated learning strategies for engagement detection that protects privacy.