Enhancing frame-level student engagement classification through knowledge transfer techniques

Assessing student engagement in educational settings is critical for monitoring and improving the learning process. Traditional methods that classify video-based student engagement datasets often assign a single engagement label to the entire video, resulting in inaccurate classification outcomes. However, student engagement varies over time, with fluctuations in concentration and interest levels. To overcome this limitation, this paper introduces a frame-level student engagement detection approach. By analyzing each frame individually, instructors gain more detailed insights into students' understanding of the course. The proposed method focuses on identifying student engagement at a granular level, enabling instructors to pinpoint specific moments of disengagement or high engagement for targeted interventions. Nevertheless, the lack of labeled frame-level engagement data presents a significant challenge. To address this challenge, we propose a novel approach for frame-level student engagement classification by leveraging the concept of knowledge transfer. Our method involves pretraining a deep learning model on a labeled image-based student engagement dataset, WACV, which serves as the base dataset for identifying frame-level engagement in our target video-based DAiSEE dataset. We then fine-tune the model on the unlabeled video dataset, utilizing the transferred knowledge to enhance engagement classification performance. Experimental results demonstrate the effectiveness of our frame-level approach, providing valuable insights for instructors to optimize instructional strategies and enhance the learning experience. This research contributes to the advancement of student engagement assessment, offering educators a more nuanced understanding of student behaviors during instructional videos.