Abstract
The COVID-19 pandemic catalyzed a significant shift towards online learning, revealing the potential of online educational videos as an educational tool in the post-crisis era. As we venture through this evolved educational terrain, it becomes crucial to understand the effectiveness and impact educational video content has on students' engagement and performance. This research explores how different styles of educational video content on STEM (Science Technology, Engineering, and Mathematics) topics impact students' engagement and comprehension using Electroencephalography (EEG) and eye-tracking data of participants viewing the educational video. In particular, we propose a machine learning driven analysis framework to study which EEG and eye-gaze-based metrics are informative of students' engagement and attention to STEM-related educational videos and predict student-population-wide comprehension. Although still in the preliminary stages, our research endeavors to identify correlations between neurophysiological patterns and educational engagement across disciplines.
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