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Interpretable Success Prediction in Higher Education Institutions Using Pedagogical Surveys

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  • Fátima Leal

    (Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
    REMIT—Research on Economics, Management and Information Technologies, 4200-072 Porto, Portugal)

  • Bruno Veloso

    (Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
    INESC TEC—Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 4200-465 Porto, Portugal)

  • Carla Santos Pereira

    (Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
    REMIT—Research on Economics, Management and Information Technologies, 4200-072 Porto, Portugal
    IJP—Instituto Jurídico Portucalense, 4200-072 Porto, Portugal)

  • Fernando Moreira

    (Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
    REMIT—Research on Economics, Management and Information Technologies, 4200-072 Porto, Portugal
    IJP—Instituto Jurídico Portucalense, 4200-072 Porto, Portugal
    IE et A—Institute of Electronics and Informatics Engineering, University of Aveiro, 3810-193 Aveiro, Portugal)

  • Natércia Durão

    (Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
    REMIT—Research on Economics, Management and Information Technologies, 4200-072 Porto, Portugal
    IJP—Instituto Jurídico Portucalense, 4200-072 Porto, Portugal)

  • Natacha Jesus Silva

    (Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
    IJP—Instituto Jurídico Portucalense, 4200-072 Porto, Portugal)

Abstract

The indicators of student success at higher education institutions are continuously analysed to increase the students’ enrolment in multiple scientific areas. Every semester, the students respond to a pedagogical survey that aims to collect the student opinion of curricular units in terms of content and teaching methodologies. Using this information, we intend to anticipate the success in higher-level courses and prevent dropouts. Specifically, this paper contributes with an interpretable student classification method. The proposed solution relies on (i) a pedagogical survey to collect student’s opinions; (ii) a statistical data analysis to validate the reliability of the survey; and (iii) machine learning algorithms to classify the success of a student. In addition, the proposed method includes an explainable mechanism to interpret the classifications and their main factors. This transparent pipeline was designed to have implications in both digital and sustainable education, impacting the three pillars of sustainability, i.e.,economic, social, and environmental, where transparency is a cornerstone. The work was assessed with a dataset from a Portuguese higher-level institution, contemplating multiple courses from different departments. The most promising results were achieved with Random Forest presenting 98% in accuracy and F -measure.

Suggested Citation

  • Fátima Leal & Bruno Veloso & Carla Santos Pereira & Fernando Moreira & Natércia Durão & Natacha Jesus Silva, 2022. "Interpretable Success Prediction in Higher Education Institutions Using Pedagogical Surveys," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13446-:d:946101
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    References listed on IDEAS

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    1. María del Carmen Olmos-Gómez & Mónica Luque Suárez & Concetta Ferrara & Eva María Olmedo-Moreno, 2020. "Quality of Higher Education through the Pursuit of Satisfaction with a Focus on Sustainability," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
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