Exploring the Viability of Using Eye Tracking to Detect Neurodivergent Learners' Implicit Learning in a Physics Game

With the prominence of assessments in education, there is an increasing need to create new forms of assessment that more accurately reflect the needs of the entire student population, particularly neurodivergent learners. To address this challenge, this paper explores the potential for using eye tracking data in a game-based learning environment to assess student's implicit knowledge. Data was collected from a sample of 66 neurodivergent college students playing the physics game Impulse while their eye movements and game play behaviors were recorded. The results indicate that gaze allocation patterns were predictive of students' physics knowledge and aligned with previously identified behavior indicators of learning. These findings provide evidence for further development of eye movement-based assessments in computer-based instruction and demonstrate how these data can be collected, organized, and analyzed.