Tailoring Personalized Mathematics Education for High School Students Using Dynamic Treatment Regimes

The current math course-taking plan for high school students across much of the United States is not optimized for every student’s success in math, especially in advanced mathematics. In particular, there has been disproportional participation in advanced math courses among different racial/ethnic and income groups. To this end, this research project develops data-driven, personalized math course-taking plans by leveraging recent advances in personalized medicine. In personalized medicine, clinicians, with the help of machine learning, use past and current historical patient data to tailor patient-specific treatment plans. In a similar vein, the proposed data-driven, personalized course-taking plan recommends appropriate math courses for students at the opportune time by using large-scale educational data from current and past students’ performances. Additionally, the project incorporates algorithmic fairness constraints from computer science and statistics to ensure that the recommendations reduce existing disparities in course-taking patterns among racial/ethnic and income groups. More broadly, with the proposed, data-driven personalized recommendations, the project hopes to transform how students take math courses (or more broadly, K-12 STEM courses) so that every student, especially students from underrepresented or disadvantaged backgrounds, will opportunities to pursue STEM majors and careers. To develop personalized math course-taking plans, the project uses a set of statistical techniques known as optimal dynamic treatment regimes (OTRs). A crucial step in using OTRs is understanding treatment effect heterogeneity from the collected data. Consequently, the first aim of the project is to leverage the existing work on using machine learning to estimate heterogeneous effects of taking different math courses in large-scale educational studies. Next, the project uses Q-learning, A-learning, and value search methods to develop OTRs, specifically sequential decisions for math course-taking. Finally, to mitigate fairness-related harms in the proposed OTRs and to ensure that the OTRs do not produce discriminatory recommendations, the project utilizes fairness constraints from the algorithmic fairness literature. The project will translate the current state-of-the-art OTR methods into K- 12 math education policies and will constitute the first attempt to integrate algorithmic fairness and optimal policy learning to design equitable policies for STEM education. The project is supported by NSF's EHR Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigator's capacity to carry out high-quality STEM education research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.