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Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?

Author

Listed:
  • Marina Segura

    (Department of Financial and Actuarial Economics & Statistics, Universidad Complutense de Madrid, 28223 Madrid, Spain)

  • Jorge Mello

    (Faculty of Exact and Technological Sciences, Universidad Nacional de Concepción, Concepción 010123, Paraguay)

  • Adolfo Hernández

    (Department of Financial and Actuarial Economics & Statistics, Universidad Complutense de Madrid, 28223 Madrid, Spain)

Abstract

University dropout rates are a problem that presents many negative consequences. It is an academic issue and carries an unfavorable economic impact. In recent years, significant efforts have been devoted to the early detection of students likely to drop out. This paper uses data corresponding to dropout candidates after their first year in the third largest face-to-face university in Europe, with the goal of predicting likely dropout either at the beginning of the course of study or at the end of the first semester. In this prediction, we considered the five major program areas. Different techniques have been used: first, a Feature Selection Process in order to identify the variables more correlated with dropout; then, some Machine Learning Models (Support Vector Machines, Decision Trees and Artificial Neural Networks) as well as a Logistic Regression. The results show that dropout detection does not work only with enrollment variables, but it improves after the first semester results. Academic performance is always a relevant variable, but there are others, such as the level of preference that the student had over the course that he or she was finally able to study. The success of the techniques depends on the program areas. Machine Learning obtains the best results, but a simple Logistic Regression model can be used as a reasonable baseline.

Suggested Citation

  • Marina Segura & Jorge Mello & Adolfo Hernández, 2022. "Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?," Mathematics, MDPI, vol. 10(18), pages 1-20, September.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3359-:d:916379
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    References listed on IDEAS

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    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    2. Alicia Nieto-Reyes & Rafael Duque & Giacomo Francisci, 2021. "A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course," Mathematics, MDPI, vol. 9(21), pages 1-14, October.
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    Cited by:

    1. Carmen Lacave & Ana Isabel Molina, 2023. "Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education," Mathematics, MDPI, vol. 11(6), pages 1-4, March.
    2. Liya Yue & Pei Hu & Shu-Chuan Chu & Jeng-Shyang Pan, 2023. "Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English," Mathematics, MDPI, vol. 11(15), pages 1-16, August.
    3. Lili Zheng & Xinyu He & Tongqiang Ding & Yanlin Li & Zhengfeng Xiao, 2022. "Analysis of the Accident Propensity of Chinese Bus Drivers: The Influence of Poor Driving Records and Demographic Factors," Mathematics, MDPI, vol. 10(22), pages 1-20, November.

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