Learner attentiveness and engagement analysis in online education using computer vision

Abstract Recently, online education and video-conferencing platforms have experienced substantial growth. Owing to the limited scope of virtual classrooms, it is difficult for instructors to assess learners’ attention and comprehension in real time during instruction. In digital learning environments, an automated feedback mechanism that informs instructors about learners’ attentiveness at any given time would be beneficial. To this end, we present a novel computer vision-based approach for analyzing learners’ affective states and quantifying their attentiveness. The foundation of our approach is a multioutput classification model trained on the publicly available DAiSEE dataset, designed to accurately identify learning-centered affective states such as boredom, engagement, confusion, and frustration. A key contribution of this work is the novel attentiveness index, which is a composite metric derived from the four affective states to quantify perceived attentiveness. Furthermore, we present a complete end-to-end pipeline that integrates this index into a practical, real-time system, processing learners’ live video feeds to provide instructors with detailed, actionable analytics. The robustness of this approach is validated by experimental outcomes in which our underlying models demonstrate state-of-the-art performance in affective state classification, thereby supporting the reliability of the entire system. The result is a comprehensive and practical real-time solution that is both deployable and easy to use, offering instructors an effective tool for gauging learner attentiveness in virtual classrooms.