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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.
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Designing Optimal, Data-Driven Policies from Multisite Randomized Trials
Optimal treatment regimes (OTRs) have been widely employed in computer science and personalized medicine to provide data-driven, optimal recommendations to individuals. However, previous research on OTRs has primarily focused on settings that are independent and identically distributed, with little attention given to the unique characteristics of educational settings, where students are nested within schools and there are hierarchical dependencies. The goal of this study is to propose a framework for designing OTRs from multisite randomized trials, a commonly used experimental design in education and psychology to evaluate educational programs. We investigate modifications to popular OTR methods, specifically Q-learning and weighting methods, in order to improve their performance in multisite randomized trials. A total of 12 modifications, 6 for Q-learning and 6 for weighting, are proposed by utilizing different multilevel models, moderators, and augmentations. Simulation studies reveal that all Q-learning modifications improve performance in multisite randomized trials and the modifications that incorporate random treatment effects show the most promise in handling cluster-level moderators. Among weighting methods, the modification that incorporates cluster dummies into moderator variables and augmentation terms performs best across simulation conditions. The proposed modifications are demonstrated through an application to estimate an OTR of conditional cash transfer programs using a multisite randomized trial in Colombia to maximize educational attainment.
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- Award ID(s):
- 2225321
- PAR ID:
- 10472447
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Psychometrika
- ISSN:
- 0033-3123
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11336-023-09937-2
