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Forecasting Students Dropout: A UTAD University Study

Author

Listed:
  • Diogo E. Moreira da Silva

    (ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal)

  • Eduardo J. Solteiro Pires

    (ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal
    INESC TEC—INESC Technology and Science (UTAD Pole), 5001-801 Vila Real, Portugal)

  • Arsénio Reis

    (ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal
    INESC TEC—INESC Technology and Science (UTAD Pole), 5001-801 Vila Real, Portugal)

  • Paulo B. de Moura Oliveira

    (ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal
    INESC TEC—INESC Technology and Science (UTAD Pole), 5001-801 Vila Real, Portugal)

  • João Barroso

    (ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal
    INESC TEC—INESC Technology and Science (UTAD Pole), 5001-801 Vila Real, Portugal)

Abstract

In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Trás-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique’s results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an F1-Score of 81% in the final test set, concluding that students’ marks somehow incorporate their living conditions.

Suggested Citation

  • Diogo E. Moreira da Silva & Eduardo J. Solteiro Pires & Arsénio Reis & Paulo B. de Moura Oliveira & João Barroso, 2022. "Forecasting Students Dropout: A UTAD University Study," Future Internet, MDPI, vol. 14(3), pages 1-14, February.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:3:p:76-:d:760686
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    References listed on IDEAS

    as
    1. 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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    Cited by:

    1. Raghul Gandhi Venkatesan & Dhivya Karmegam & Bagavandas Mappillairaju, 2024. "Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis," Journal of Computational Social Science, Springer, vol. 7(1), pages 171-196, April.
    2. Ivan Miguel Pires, 2022. "Smart Objects and Technologies for Social Good," Future Internet, MDPI, vol. 14(12), pages 1-3, December.
    3. Isaac Caicedo-Castro, 2023. "Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study," Sustainability, MDPI, vol. 15(18), pages 1-18, September.

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