Improving Models for Student Retention and Graduation using Markov Chains

02/01/2023

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by   Mason N Tedeschi, et al.

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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 results indicate that Learning Assistants can improve overall graduation rates and address inequalities in graduation rates for underrepresented students.