Improving models for student retention and graduation using Markov chains

Mason N Tedeschi, Tiana M Hose, Emily K Mehlman, Scott Franklin and Tony E Wong

PLOS ONE, 2023, vol. 18, issue 6, 1-14

Abstract: Graduation rates are a key measure of the long-term efficacy of academic interventions. However, challenges to using traditional estimates of graduation rates for underrepresented students include inherently small sample sizes and high data requirements. Here, we show that a Markov model increases confidence and reduces biases in estimated graduation rates for underrepresented minority and first-generation students. We use a Learning Assistant program to demonstrate the Markov model’s strength for assessing program efficacy. We find that Learning Assistants in gateway science courses are associated with a 9% increase in the six-year graduation rate. These gains are larger for underrepresented minority (21%) and first-generation students (18%). Our results indicate that Learning Assistants can improve overall graduation rates and address inequalities in graduation rates for underrepresented students.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0287775

DOI: 10.1371/journal.pone.0287775

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