Using a Webcam Based Eye-tracker to Understand Students' Thought Patterns and Reading Behaviors in Neurodivergent Classrooms

Previous learning analytics efforts have attempted to leverage the link between students' gaze behaviors and learning experiences to build effective real-time interventions. Historically, however, these technologies have not been scalable due to the high cost of eye-tracking devices. Further, such efforts have been almost exclusively focused on neurotypical students, despite recent work that suggests a "one size fits many"approach can disadvantage neurodivergent students. Here we attempt to address these limitations by examining the validity and applicability of using scalable, webcam-based eye tracking as a basis for adaptively responding to neurodivergent students in an educational setting. Forty-three neurodivergent students read a text and answered questions about their in-situ thought patterns while a webcam-based eye tracker assessed their gaze locations. Results indicate that eye-tracking measures were sensitive to: 1) moments when students experienced difficulty disengaging from their own thoughts and 2) students' familiarity with the text. Our findings highlight the fact that a free, open-source, webcam-based eye-tracker can be used to assess differences in reading patterns and online thought patterns. We discuss the implications and possible applications of these results, including the idea that webcam-based eye tracking may be a viable solution for designing real-time interventions for neurodivergent student populations.