Games in service of learning are uniquely positioned to offer immersive, interactive educational experiences. Well-designed games build challenge through a series of well-ordered problems or activities, in which perseverance is key for working through ingame failure and increasing game difficulty. Indeed, persistence through challenges during learning is beneficial not just in games but in other contexts as well, with grit and perseverance positively associated with academic performance and learning outcomes. However, recent studies suggest that not all persistence is positive, suggesting that many students end up "wheel-spinning", spending considerable time on a topic without achieving mastery. Thus, it is vital to differentiate productive and unproductive persistence in order to understand emergent student progress, particularly in the context of learning games and personalized learning systems, in which individual pathways differ greatly based on student needs. Leveraging Educational Data Mining methods, this study builds a detector of wheel-spinning behavior (differentiated from productive persistence) in an adaptive, game-based learning system. With the ability to predict unproductive persistence early, this detection model can be used to intelligently adapt to students needing further support in-system, as well as informing in-person intervention in a classroom setting--thus supporting a personalized, engaging learning experience in both formal and informal learning environments. [For the full proceedings, see ED599096.]