In this thesis we find an optimal treatment regime (OTR) 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. We leverage tree-based reinforcement learning approaches developed in the recent literature based on either an inverse probability weighted purity measure or an augmented probability weighted purity measure. The thereby deduced OTR promises to increase significantly the average grade in the introductory course and also shows the need for program recommendations to students as only very few, on their own selected their optimal treatment.
