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Predictors of University Attrition: Looking for an Equitable and Sustainable Higher Education

Author

Listed:
  • Jack Vidal

    (Educational Research Group INEDU-EPN, Escuela Politécnica Nacional, Quito 17-01-2759, Ecuador)

  • Raquel Gilar-Corbi

    (Department of Developmental Psychology and Didactics, University of Alicante, 03080 Alicante, Spain)

  • Teresa Pozo-Rico

    (Department of Developmental Psychology and Didactics, University of Alicante, 03080 Alicante, Spain)

  • Juan-Luis Castejón

    (Department of Developmental Psychology and Didactics, University of Alicante, 03080 Alicante, Spain)

  • Tarquino Sánchez-Almeida

    (Educational Research Group INEDU-EPN, Escuela Politécnica Nacional, Quito 17-01-2759, Ecuador)

Abstract

The failure and dropout of university studies are issues that worry all nations due to the personal, social, and economic costs that this they entail. Because the dropout phenomenon is complex and involves numerous factors, to reverse it would involve a comprehensive approach through interventions aimed at the factors identified as key in the decision to drop out. Therefore, the main objective of this work is to determine the profile of students who enter the EPN (STEM higher-education institution) to analyze the characteristics that differentiate students who drop out early in their career and those who stay in school. A sample of 624 students who accessed the EPN leveling course (a compulsory course at the beginning of their studies) participated in the study. A total of 26.6% of the participants were women. A total of 50.7% of the participants passed the course. Data referring to social, economic, and academic variables were analyzed. Comparison techniques, as well as artificial neural networks, were used to compare characteristic profiles of students who passed the leveling course and those who dropped out. The results showed significant differences between the profiles of the students who passed and those who dropped out with regard to the variables related to previous academic performance and motivational and attributional aspects. The artificial neural networks corroborated the importance of these variables in predicting dropout. In this research, the key variables predicting whether a student continues or leaves higher education are revealed, allowing the identification of students at possible risk of dropping out and thus promoting initiatives to provide adequate academic support and improve student retention.

Suggested Citation

  • Jack Vidal & Raquel Gilar-Corbi & Teresa Pozo-Rico & Juan-Luis Castejón & Tarquino Sánchez-Almeida, 2022. "Predictors of University Attrition: Looking for an Equitable and Sustainable Higher Education," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10994-:d:905669
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    References listed on IDEAS

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    1. Jorge Maluenda-Albornoz & Valeria Infante-Villagrán & Celia Galve-González & Gabriela Flores-Oyarzo & José Berríos-Riquelme, 2022. "Early and Dynamic Socio-Academic Variables Related to Dropout Intention: A Predictive Model Made during the Pandemic," Sustainability, MDPI, vol. 14(2), pages 1-18, January.
    2. Bird, Kate & Chabé-Ferret, Bastien & Simons, Alexandre, 2022. "Linking human capabilities with livelihood strategies to speed poverty reduction: Evidence from Rwanda," World Development, Elsevier, vol. 151(C).
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