My Master’s Thesis investigated the usefulness of different data sources for automatically predicting when students using an Intelligent Tutoring System were engaged and curious, or disengaged and bored. Detailed comparisons of machine learning algorithms trained with eye-tracking data, Electrodermal Activity (EDA) and distance from the screen revealed that distance (which can be obtained with cheap infra-red sensors) provided one of the simplest and most reliable signals of student engagement.