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Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities

Author

Listed:
  • Diego Opazo

    (Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Viña del Mar 2520000, Chile)

  • Sebastián Moreno

    (Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Viña del Mar 2520000, Chile)

  • Eduardo Álvarez-Miranda

    (School of Economics and Business, Universidad de Talca, Talca 3460493, Chile
    Instituto Sistemas Complejos de Ingeniería, Santiago 8370398, Chile)

  • Jordi Pereira

    (Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Viña del Mar 2520000, Chile)

Abstract

Student dropout, defined as the abandonment of a high education program before obtaining the degree without reincorporation, is a problem that affects every higher education institution in the world. This study uses machine learning models over two Chilean universities to predict first-year engineering student dropout over enrolled students, and to analyze the variables that affect the probability of dropout. The results show that instead of combining the datasets into a single dataset, it is better to apply a model per university. Moreover, among the eight machine learning models tested over the datasets, gradient-boosting decision trees reports the best model. Further analyses of the interpretative models show that a higher score in almost any entrance university test decreases the probability of dropout, the most important variable being the mathematical test. One exception is the language test, where a higher score increases the probability of dropout.

Suggested Citation

  • Diego Opazo & Sebastián Moreno & Eduardo Álvarez-Miranda & Jordi Pereira, 2021. "Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities," Mathematics, MDPI, vol. 9(20), pages 1-27, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2599-:d:657400
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    References listed on IDEAS

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    1. Behr Andreas & Giese Marco & Teguim K Herve D. & Theune Katja, 2020. "Early Prediction of University Dropouts – A Random Forest Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(6), pages 743-789, December.
    2. Paula Giovagnoli, 2005. "Determinants in University Desertion and Graduation: An Application using Duration Models," Económica, Instituto de Investigaciones Económicas, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 0(1-2), pages 59-90, January-D.
    3. Paula Giovagnoli, 2005. "Determinants in University Desertion and Graduation: An Application using Duration Models," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 0(1-2), pages 59-90, January-D.
    4. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    5. Catalina A. Vallejos & Mark F. J. Steel, 2017. "Bayesian survival modelling of university outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 613-631, February.
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