Data Mining and Machine Learning Retention Models in Higher Education

Autor/inn/en	Cardona, Tatiana; Cudney, Elizabeth A.; Hoerl, Roger; Snyder, Jennifer
Titel	Data Mining and Machine Learning Retention Models in Higher Education
Quelle	In: Journal of College Student Retention: Research, Theory & Practice, 25 (2023) 1, S.51-75 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Cudney, Elizabeth A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1521-0251
DOI	10.1177/1521025120964920
Schlagwörter	Learning Analytics; Data Analysis; Prediction; Higher Education; Academic Persistence; Dropouts; Risk; Classification; Graduation Rate; Barriers; Academic Advising; Educational Strategies; Intervention; Research Reports; Grade Point Average; Student Financial Aid; Student Characteristics; College Entrance Examinations; College Credits; SAT (College Admission Test) + Suchen Sie Ihr Suchwort? Auswertung; Vorhersage; Hochschulbildung; Hochschulsystem; Hochschulwesen; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Risiko; Classification system; Klassifikation; Klassifikationssystem; Akademischer Rat; Lehrstrategie; Research report; Forschungsbericht; Finanzielle Beihilfe; Studienfinanzierung; Studienförderung; Aufnahmeprüfung; College; Colleges; Achievement; Performance; Anrechnung; Hochschule; Fachhochschule; Leistung
Abstract	This study presents a systematic review of the literature on the predicting student retention in higher education through machine learning algorithms based on measures such as dropout risk, attrition risk, and completion risk. A systematic review methodology was employed comprised of review protocol, requirements for study selection, and analysis of paper classification. The review aims to answer the following research questions: (1) what techniques are currently used to predict student retention rates, (2) which techniques have shown better performance under specific contexts?, (3) which factors influence the prediction of completion rates in higher education?, and (4) what are the challenges with predicting student retention? Increasing student retention in higher education is critical in order to increase graduation rates. Further, predicting student retention provides insight into opportunities for intentional student advising. The review provides a research perspective related to predicting student retention using machine learning through several key findings such as the identification of the factors utilized in past studies and methodologies used for prediction. These findings can be used to develop more comprehensive studies to further increase the prediction capability and; therefore, develop strategies to improve student retention. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01