Prediction of Student Attrition Risk Using Machine Learning

Autor/inn/en	Barramuño, Mauricio; Meza-Narváez, Claudia; Gálvez-García, Germán
Titel	Prediction of Student Attrition Risk Using Machine Learning
Quelle	In: Journal of Applied Research in Higher Education, 14 (2022) 3, S.974-986 (13 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Barramuño, Mauricio) ORCID (Gálvez-García, Germán)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2050-7003
DOI	10.1108/JARHE-02-2021-0073
Schlagwörter	Student Attrition; School Holding Power; Foreign Countries; Physical Therapy; Allied Health Occupations Education; Artificial Intelligence; Man Machine Systems; College Students; At Risk Students; Predictive Measurement; Chile + Suchen Sie Ihr Suchwort? Schülerbeurlaubung; Ausland; Physiotherapie; Künstliche Intelligenz; Mensch-Maschine-System; Collegestudent
Abstract	Purpose: The prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program. Design/methodology/approach: Machine learning is a computer tool that can recognize patterns and generate predictive models. Using a quantitative research methodology, a database of 336 university students in their upper-year courses was accessed. The participant's data were collected from the Financial Academic Management and Administration System and a platform of Universidad Autónoma de Chile. Five quantitative and 11 qualitative variables were chosen, associated with university student attrition. With this database, 23 classifiers were tested based on supervised machine learning. Findings: About 23.58% of males and 17.39% of females were among the attrition student group. The mean accuracy of the classifiers increased based on the number of variables used for the training. The best accuracy level was obtained using the "Subspace KNN" algorithm (86.3%). The classifier "RUSboosted trees" yielded the lowest number of false negatives and the higher sensitivity of the algorithms used (78%) as well as a specificity of 86%. Practical implications: This predictive method identifies attrition students in the university program and could be used to improve student retention in higher grades. Originality/value: The study has developed a novel predictive model of student attrition from upper-year courses, useful for unbalanced databases with a lower number of attrition students. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01