One Size Does Not Fit All: Considerations when using Webcam-Based Eye Tracking to Models of Neurodivergent Learners’ Attention and Comprehension

This study investigates the use of webcam-based eye tracking to model attention and comprehension in both neurotypical and neurodivergent learners. Leveraging the WebGazer, a previously used online data collection tool, we collected gaze and interaction data (N=354) during online reading tasks to explore task unrelated thought (TUT) and comprehension in an ecologically valid setting. Our findings challenge the "one size fits all" approach to learner modeling by demonstrating distinct differences in indicators of both constructs between neurotypical and neurodivergent learners. We compared general models trained on the entire population with tailored models specific to neurodivergent and neurotypical groups. Results indicate that diagnosis-specific models provide more accurate predictions (AUROC's .59-.70 vs. .57 for the general model), and through SHAPley analysis, we note that the strongest indicators of each construct vary as the training population is refined, highlighting the limitations of generalized approaches. This work supports the scalability of webcam-based cognitive modeling and underscores the potential for personalized learning analytics and modeling to better support diverse learning needs.