Eye-tracking as a source of information for automatically predicting user learning with MetaTutor, an intelligent tutoring system to support self-regulated learning

Student modeling has been gaining interest among researchers recently. A lot of work has been done on exploring value of interface actions on predicting learning. The focus of this thesis is on using eye-tracking data and action logs for building classifies to infer a student’s learning performance during interaction with MetaTutor, an Intelligent Tutoring System( ITS) that scaffolds self-regulated learning (SRL). Research has shown that eye tracking can be a valuable source for predicting learning for certain learning environments. In this thesis we extend these results by showing that modeling based on eye-tracking data is a valuable approach to predicting learning for another type of ITS, a hypermedia learning environment. We use data from 50 students (collected by a research team at McGill University, which also designed MetaTutor) to compare the performance of actions and eye-tracking data (1) after a complete interaction, and (2) during interaction when different amounts of gaze and action data are available. We built several classifiers using common machine learning algorithms and techniques, with feature sets that are based on (1) eye-tracking data only, (2) actions data only and (3) eye-tracking and actions data combined. The results we found show that eye-tracking data brings important information in predicting student’s performance for an ITS supporting SRL in both overall and over time analysis. The features used for training classifiers suggest that usage of SRL tools available in MetaTutor can be a good predictor of learning.Science, Faculty ofComputer Science, Department ofGraduat