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How to customize an early start preparatory course policy to improve student graduation success: an application of uplift modeling

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  • Yertai Tanai

    (California State University)

  • Kamil Ciftci

    (California State University)

Abstract

Improving student graduation is of utmost importance for higher education institutions, both public and private. The key contribution of this study is to apply the uplift modeling framework to optimize preparatory course assignments as an instrument to boost student success for graduation. Specifically, we concentrate on two university programs, English and Math preparatory courses, to identify students who would benefit the most from these courses and successfully graduate. To achieve this objective, we analyze 10-years of incoming freshmen data with a wide range of feature variables from a major university in the US. We then build and test several uplift methodologies to determine students’ response to the treatments. The best-performing model allows us to identify students in different segments and target those who are most responsive to the treatment to achieve optimal results. Additionally, we identify the most significant variables and provide student profiles and attributes that distinguish those who would gain from preparatory courses from those who would not. The framework developed in this study can serve as a valuable tool for decision-making and policy support. It can improve not only the student success perspective, but also the allocation of university resources by identifying and advising a fraction of students who would benefit from taking these preparatory courses and have a positive impact on their graduation.

Suggested Citation

  • Yertai Tanai & Kamil Ciftci, 2025. "How to customize an early start preparatory course policy to improve student graduation success: an application of uplift modeling," Annals of Operations Research, Springer, vol. 347(2), pages 913-936, April.
    • Handle: RePEc:spr:annopr:v:347:y:2025:i:2:d:10.1007_s10479-023-05607-9
    DOI: 10.1007/s10479-023-05607-9
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    References listed on IDEAS

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    1. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    2. Delen, Dursun & Topuz, Kazim & Eryarsoy, Enes, 2020. "Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition," European Journal of Operational Research, Elsevier, vol. 281(3), pages 575-587.
    3. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.
    4. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
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