Leveraging Eye Tracking in Digital Classrooms: A Step Towards Multimodal Model for Learning Assistance

Abstract

Instructors who teach digital literacy skills are increasingly faced with the challenges that come with larger student populations and online courses. We asked an educator how we could support student learning and better assist instructors both online and in the classroom. To address these challenges, we discuss how behavioral signals collected from eye tracking and mouse tracking can be combined to offer predictions of student performance. In our preliminary study, participants completed two image masking tasks in Adobe Photoshop based on real college-level course content. We then trained a machine learning model to predict student performance in each task based on data from other students, as a step towards offering automated student assistance and feedback to instructors. We reflect on the challenges and scalability issues to deploying such a system in-the-wild, and present some guidelines for future work.