Predictors of Freshmen Attrition: A Case Study of Bayesian Methods and Probabilistic Programming

Eitel Lauría

2025

Abstract

The study explores the use of Bayesian hierarchical linear models to make inferences on predictors of freshmen student attrition using student data from nine academic years and six schools at Marist University. We formulate a hierarchical generalized (Bernoulli) linear model, and implement it in a probabilistic programming platform using Markov chain Monte Carlo (MCMC) techniques. Model fitness, parameter convergence, and the significance of regression estimates are assessed. We compared the Bayesian model to a frequentist generalized linear mixed model (GLMM). We identified college academic performance, financial need, gender, tutoring, and work-study program participation as significant factors affecting the log-odds of freshmen attrition. Additionally, the study revealed fluctuations across time and schools. The variation in attrition rates highlights the need for targeted retention initiatives, as some schools appear more vulnerable to higher attrition. The study provides valuable insights for stakeholders, administrators, and decision-makers, offering applicable findings for other institutions and a detailed guideline on analyzing educational data using Bayesian methods.

Paper Citation

in Harvard Style

Lauría E. (2025). Predictors of Freshmen Attrition: A Case Study of Bayesian Methods and Probabilistic Programming. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 120-131. DOI: 10.5220/0013555800003967

in Bibtex Style

@conference{data25,
author={Eitel Lauría},
title={Predictors of Freshmen Attrition: A Case Study of Bayesian Methods and Probabilistic Programming},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={120-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013555800003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Predictors of Freshmen Attrition: A Case Study of Bayesian Methods and Probabilistic Programming
SN - 978-989-758-758-0
AU - Lauría E.
PY - 2025
SP - 120
EP - 131
DO - 10.5220/0013555800003967
PB - SciTePress