Improving Models for Student Retention and Graduation using Markov Chains

TL;DR

This paper demonstrates that Markov models can improve the accuracy of graduation rate estimates, especially for underrepresented students, by reducing biases and increasing confidence in the results.

Contribution

The study introduces a Markov modeling approach to better estimate graduation rates for underrepresented students, addressing limitations of traditional methods.

Findings

Learning Assistants increase six-year graduation rates by 9%.

Underrepresented minority students see a 21% increase.

First-generation students experience an 18% increase.

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.