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Prediction of student attrition risk using machine learning

Mauricio Barramuño (Carrera Kinesiología, Sede Temuco, Universidad Autónoma de Chile, Temuco, Chile)
Claudia Meza-Narváez (Carrera Kinesiología, Sede Temuco, Universidad Autónoma de Chile, Temuco, Chile)
Germán Gálvez-García (Departamento de Psicología, Universidad de La Frontera, Temuco, Chile) (Laboratoired’Étude des Mécanismes Cognitifs, Département de Psychologie Cognitive, Sciences Cognitives et Neuropsychologie, Institut de Psychologie, Université Lyon 2, Lyon, France)

Journal of Applied Research in Higher Education

ISSN: 2050-7003

Article publication date: 20 May 2021

Issue publication date: 31 May 2022




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.


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.


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.


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.



Financing: This study was financed by the Teaching Innovation and Development Fund from the Vice-Rector for Academic Affairs and the Vice-Rector of Research and Graduate Studies at the Universidad Autónoma de Chile.

Conflict of interest: The authors declare no conflict of interest.


Barramuño, M., Meza-Narváez, C. and Gálvez-García, G. (2022), "Prediction of student attrition risk using machine learning", Journal of Applied Research in Higher Education, Vol. 14 No. 3, pp. 974-986.



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