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Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques

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  • Daniel Zapata-Medina

    (Department of Computer and Decision Sciences, Faculty of Mines, Universidad Nacional de Colombia, Medellín 050034, Colombia)

  • Albeiro Espinosa-Bedoya

    (Department of Computer and Decision Sciences, Faculty of Mines, Universidad Nacional de Colombia, Medellín 050034, Colombia)

  • Jovani Alberto Jiménez-Builes

    (Department of Computer and Decision Sciences, Faculty of Mines, Universidad Nacional de Colombia, Medellín 050034, Colombia)

Abstract

The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conducted by The Organization for Economic Co-operation and Development. This study compares five feature selection techniques to address this challenge and improve the automatic detection of dropout risk. The methodological design involves three distinct phases: data preparation, feature selection, and model evaluation utilizing machine learning algorithms. The results demonstrate that (1) the top features identified by feature selection techniques, i.e., those constructed through feature engineering, proved to be among the most effective in classifying student dropout; (2) the F-score of the best model increased by 5% with feature selection techniques; and (3) depending on the type of feature selection, the performance of the machine learning algorithm can vary, potentially increasing or decreasing based on the sensitivity of features with higher noise. At the same time, metaheuristic algorithms demonstrated significant precision improvements, but there was a risk of increasing errors and reducing recall.

Suggested Citation

  • Daniel Zapata-Medina & Albeiro Espinosa-Bedoya & Jovani Alberto Jiménez-Builes, 2024. "Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques," Mathematics, MDPI, vol. 12(12), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1776-:d:1410836
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    References listed on IDEAS

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    1. Delen, Dursun & Topuz, Kazim & Eryarsoy, Enes, 2020. "Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition," European Journal of Operational Research, Elsevier, vol. 281(3), pages 575-587.
    2. Chung, Jae Young & Lee, Sunbok, 2019. "Dropout early warning systems for high school students using machine learning," Children and Youth Services Review, Elsevier, vol. 96(C), pages 346-353.
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