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Recent research has indicated that misuse of intelligent tutoring software is correlated with substantially lower learning. Students who frequently engage in behavior termed “gaming the system” (behavior aimed at obtaining correct answers and advancing within the tutoring curriculum by systematically taking advantage of regularities in the software’s feedback and help) learn only 2/3 as much as similar students who do not engage in such behaviors. We present a machine-learned Latent Response Model that can identify if a student is gaming the system in a way that leads to poor learning. We believe this model will be useful both for re-designing tutors to respond appropriately to gaming, and for understanding the phenomenon of gaming better.