Predicting Dropout for Nontraditional Undergraduate Students: A Machine Learning Approach

Autor/inn/en	Huo, Huade; Cui, Jiashan; Hein, Sarah; Padgett, Zoe; Ossolinski, Mark; Raim, Ruth; Zhang, Jijun
Titel	Predicting Dropout for Nontraditional Undergraduate Students: A Machine Learning Approach
Quelle	In: Journal of College Student Retention: Research, Theory & Practice, 24 (2023) 4, S.1054-1077 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Padgett, Zoe)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1521-0251
DOI	10.1177/1521025120963821
Schlagwörter	Student Attrition; Postsecondary Education; Nontraditional Students; Dropout Rate; Predictive Measurement; At Risk Students; Artificial Intelligence + Suchen Sie Ihr Suchwort? Schülerbeurlaubung; Post-secondary education; Tertiäre Bildung; Künstliche Intelligenz
Abstract	Student attrition represents one of the greatest challenges facing U.S. postsecondary institutions. Approximately 40 percent of students seeking a bachelor's degree do not graduate within 6 years; among nontraditional students, who make up half of the undergraduate population, dropout rates are even higher. In this study, we developed a machine learning classifier using the XGBoost model and data from the National Center for Education Statistics (NCES) Beginning Postsecondary Students (BPS) Longitudinal Study: 2012/14 to predict nontraditional student dropout. In comparison with baseline models, the XGBoost model and logistic regression model with features identified by the XGBoost model displayed superior performance in predicting dropout. The predictive ability of the model and the features it identified as being most important in predicting nontraditional student dropout can inform discussion among educators seeking ways to identify and support at-risk students early in their postsecondary careers. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01