Machine Learning in Predicting Undergraduate Student Attrition in the United States: A Scoping Review

Abstract

Attrition among undergraduate students contributes to lower retention rates across U.S. higher education institutions (public and private colleges and universities), resulting in academic, financial, and institutional consequences. This scoping review maps evidence on the use of machine learning (ML) models to predict undergraduate student attrition in the United States. This review also appraises the quality of reporting in the included studies using the TRIPOD + AI checklist. We searched five electronic databases for articles covering this domain, and 14 studies met the inclusion criteria. This review mapped studies according to data sources, attrition indicators, patterns, ML models, and modeling techniques. Across the included studies, attrition varied from one semester to multiple years, and random forest and logistic regression were the most frequently used ML models. This review identifies attrition patterns, ML approaches used for predicting attrition, highlights implications for researchers and practice utilizing ML for attrition prediction, and proposes directions for future research.