Bayesian Survival Modelling of University Outcomes

TL;DR

This paper develops a Bayesian competing risks survival model to analyze university enrollment durations and outcomes, addressing dropout and graduation processes with model uncertainty handled via Bayesian model averaging.

Contribution

It introduces a Bayesian survival analysis framework for university outcomes, incorporating competing risks and model averaging to identify key determinants.

Findings

Identified significant factors influencing dropout and graduation.

Demonstrated the model's effectiveness on three degree programs.

Provided insights into university student retention and success.

Abstract

The aim of this paper is to model the length of registration at university and its associated academic outcome for undergraduate students at the Pontificia Universidad Cat\'olica de Chile. Survival time is defined as the time until the end of the enrollment period, which can relate to different reasons - graduation or two types of dropout - that are driven by different processes. Hence, a competing risks model is employed for the analysis. The issue of separation of the outcomes (which precludes maximum likelihood estimation) is handled through the use of Bayesian inference with an appropriately chosen prior. We are interested in identifying important determinants of university outcomes and the associ- ated model uncertainty is formally addressed through Bayesian model averaging. The methodology introduced for modelling university outcomes is applied to three selected degree programmes, which are particularly affected by dropout and late graduation.