Predicting Student Dropout in Higher Education

TL;DR

This study uses machine learning on a large university dataset to predict student dropout, identifying early indicators and demonstrating high prediction accuracy from limited transcript data, aiming to improve student retention strategies.

Contribution

It introduces a novel application of machine learning to predict student dropout using extensive transcript data, highlighting early indicators and potential for intervention.

Findings

Dropout can be predicted accurately from a single term of transcript data.

Several early academic indicators are associated with student attrition.

Machine learning shows promise for improving student retention strategies.

Abstract

Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model student dropout using the largest known dataset on higher education attrition, which tracks over 32,500 students' demographics and transcript records at one of the nation's largest public universities. Our results highlight several early indicators of student attrition and show that dropout can be accurately predicted even when predictions are based on a single term of academic transcript data. These results highlight the potential for machine learning to have an impact on student retention and success while pointing to several promising directions for future work.