Propensity score matching and weighting methods are applied to balance covariates and reduce selection bias in the analysis of observational study data, and ultimately estimate a treatment effect. We wish to evaluate the impact of a Supplemental Instruction (SI) program on student success in an Introductory Statistics course. In such student success studies, propensity score methods have been applied successfully for evaluating a binary treatment, namely whether a student attending SI performs better or worse than a student who does not attend SI. However, in this setting, we also want to draw inferences on the dose-response relationship, namely how does the number of times a student attends SI impact performance in the course. In this paper, we introduce generalized propensity scores (GPS) for analyzing such continuous treatment. We extend recent developments in GPS analyses from the personalized learning literature for evaluating SI engagement on student success. As part of the exposition, we provide a brief review of generalized propensity scores, compare our proposed GPS approaches, present guidelines on how these methods can be applied to educational data, and present R code and illustration for practitioners to use as a template in educational data mining applications.