Improving Models for Student Retention and Graduation using Markov Chains
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.
Cite
@article{arxiv.2302.00464,
title = {Improving Models for Student Retention and Graduation using Markov Chains},
author = {Mason N Tedeschi and Tiana M Hose and Emily K Mehlman and Scott Franklin and Tony E Wong},
journal= {arXiv preprint arXiv:2302.00464},
year = {2023}
}