Synchronized eye movements predict test scores in online video education

Abstract

Abstract Experienced teachers pay close attention to their students, adjusting their teaching when students seem lost. This dynamic interaction is missing in online education. We propose to measure attention to online videos remotely by tracking eye movements, as we hypothesize that attentive students follow videos similarly with their eyes. Here we show that inter-subject correlation of eye-movements during instructional video presentation is substantially higher for attentive students, and that synchronized eye movement are predictive of individual test scores on the material presented in the video. These findings replicate for videos in a variety of production styles, learning scenarios and for recall and comprehension questions alike. We reproduce the result using standard web cameras to capture eye-movements in a classroom setting, and with over 1,000 participants at-home without the need to transmit user data. Our results suggest that online education could be made adaptive to a student’s level of attention in real-time.