Live Monitoring of Student Behaviour During Online Classes

With the rapid shift towards virtual education, ensuring student active participation during online classes has become a significant challenge for institutions. This paper presents a real-time system for monitoring student behaviour using computer vision techniques to assess student engagement during online classes. The proposed system employs facial expression analysis, eye movement tracking, and head posture detection to determine levels of engagement, drowsiness, and distraction of the students. A web-based interface captures input from webcam and processes it using image processing methods. The system provides instructors with a live dashboard displaying behavioural insights, enabling timely intervention and improved classroom management. This study aims to enhance online learning experiences by providing a seamless tool for student behaviour assessment.