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We expand methods for estimating an optimal treatment regime (OTR) from the personalized medicine literature to educational data mining applications. As part of this development, we detail and modify the current state-of-the-art, assess the efcacy of the approaches for student success studies, and provide practitioners the machinery to apply the methods in their specifc problems. Our particular interest is to estimate an optimal treatment regime for students enrolled in an introductory statistics course at San Diego State University (SDSU). The available treatments are combinations of three programs SDSU implemented to foster student success in this large enrollment, bottleneck STEM course. We leverage tree-based reinforcement learning approaches based on either an inverse probability-weighted purity measure or an augmented probability-weighted purity measure. The thereby deduced OTR promises to signifcantly increase the average grade in the introductory course and also reveals the need for program recommendations to students as only very few, on their own, selected their optimal treatment.