The purpose of this study was to investigate students’ patterns of interactions within a game-based intelligent tutoring system (ITS), and how those interactions varied as a function of individual differences. The analysis presented in this paper comprises a subset (n=40) of a larger study that included 124 high school students. Participants in the current study completed 11 sessions within iSTART-ME, a game-based ITS, that provides training in reading comprehension strategies. A random walk analysis was used to visualize students’ trajectories within the system. The analyses revealed that low ability students’ patterns of interactions were anchored by one feature category whereas high ability students demonstrated interactions across multiple categories. The results from the current paper indicate that random walk analysis is a promising visualization tool for learning scientists interested in capturing students’ interactions within ITSs and other computer-based learning environments over time.