Automatic detection of student off-task behavior while using an intelligent tutor for algebra

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As more and more modern classrooms use intelligent tutoring systems, it becomes imperative for our educators to determine whether these systems are being used properly. While using an intelligent tutor, it is possible for students to engage in off-task behavior, defined as actions that show disengagement from learning. Off-task behavior can range from resting one's eyes, to talking to one's seatmate, to "gaming the system" defined as abusing regularities of the intelligent tutor to progress through the curriculum without actually learning the material. These behaviors constitute time away from the learning task and are therefore considered detrimental to learning. In this paper, we attempt to create a model that automatically detects learner offtask behavior while using Aplusix, an intelligent tutor for algebra. By analyzing logs of interactions recorded by the Aplusix, we determine off-task behavior’s quantifiable characteristics. Afterwards, we use machine learning techniques to create a model of off-task behavior. Automatic detection can lead to interventions that can retain student attention and increase learning.