The Eyes Have It: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System

Mind wandering (MW) is a ubiquitous phenomenon characterized by an unintentional shift in attention from task-related to taskunrelated thoughts. MW is frequent during learning and negatively correlates with learning outcomes. Therefore, the next generation of intelligent learning technologies should benefit from mechanisms that detect and combat MW. As an initial step in this direction, we used eye-gaze and contextual information (e.g., time into session) to build an automated MW detector as students interact with GuruTutor – an intelligent tutoring system (ITS) for biology. Students self-reported MW by responding to pseudorandom thought-probes during the tutoring session while a consumer-grade eye tracker monitored their eye movements. We used supervised machine learning techniques to discriminate between positive and negative responses to the probes in a student-independent fashion. Our best results for detecting MW (F1 of 0.49) were obtained with an evolutionary approach to develop topologies for neural network classifiers. These outperformed standard classifiers (F1 of 0.43 with a Bayes net) and a chance baseline (F1 of 0.19). We discuss our results in the context of integrating MW detection into an attention-aware version of GuruTutor.