Using Webcam-Based Eye Tracking during a Learning Task to Understand Neurodivergence

This study explores the use of webcam-based eye tracking during a learning task to predict and better understand neurodivergence with the aim of improving personalized learning to support diverse learning needs. Using WebGazer, a webcam-based eye tracking technology, we collected gaze data from 354 participants as they engaged in educational online reading. We extracted both gaze features and text characteristics, as well as interactions between gaze and text. Results show that the supervised machine-learned model predicting whether a learner is neurodivergent or not achieved an AUROC of 0.60 and a Kappa of 0.14, indicating slight agreement beyond chance. For specific neurodivergent diagnoses, AUROC values ranged from 0.53 to 0.61, demonstrating moderate predictive performance. Additionally, SHAP analysis was used to examine the influence of features selected through forward feature selection, revealing both commonalities and differences between predicting broad neurodivergence and specific diagnoses. These findings should not be used for diagnostic purposes or to single out any individual but instead underscore the potential for personalized modeling to better support diverse learning needs.