Automated Detection of Attention and Retention in Educational Videos Using Eye-Tracking, Dynamic Areas of Interest and Feature Fusion.

Educational videos are widely used in remote and blended learning. However, learners' attention often fluctuates while watching, which can hinder their retention of key information. This, in turn, may impact their overall learning outcomes. Detecting when learners lose attention or fail to memorize key elements of a video could help address these challenges-for example, by enabling adaptive support that enhances engagement and retention, bridging the gap between passive video consumption and active learning. Such automated detection could also provide valuable insights to instructors about when attention and retention drop in their videos. In this study, we explore how to detect learners' attention and retention while they watch an educational video in a blended course on green chemistry, using eye-tracking data. To achieve this, we develop machine learning classifiers that analyze eye movements, pupil dilation, eye-screen distance, and attention to dynamically tracked areas of interest (AOIs). We investigate different strategies for fusing these types of information and find that dynamic AOIs can significantly improve ML predictions, albeit with moderate performance.