In this thesis we investigate the usefulness of various data sources for predicting emotions relevant to learning, specifically boredom and curiosity. The data was collected during a study with MetaTutor, an intelligent tutoring system (ITS) designed to promote the use of self-regulated learning strategies. We used a variety of machine learning and feature selection techniques to predict students‘ self-reported emotions from eye tracking data, distance from the screen, electrodermal activity, and an ensemble of all three sources. We also examine the optimal amount of interaction time needed to make predictions using each source, as well as which gaze features are most predictive of each emotion. The findings provide insight into how to detect when students disengage from MetaTutor.