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A major issue in Intelligent Tutoring Systems is off-task student behavior, especially performance-based gaming, where students systematically exploit tutor behavior in order to advance through a curriculum quickly and easily, with as little active thought directed at the educational content as possible. The goal of this research was to explore the phenomena of off-task gaming behavior within the Assistments system. Machine-learned gaming-detection models were developed to investigate underlying factors related to gaming, and an analysis of gaming within the Assistments system was conducted to compare some of the findings of prior studies.