Interpretable Success Prediction in Higher Education Institutions Using Pedagogical Surveys

Fátima Leal, Bruno Veloso, Carla Santos Pereira, Fernando Moreira (), Natércia Durão and Natacha Jesus Silva
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Fátima Leal: Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
Bruno Veloso: Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
Carla Santos Pereira: Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
Fernando Moreira: Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
Natércia Durão: Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal
Natacha Jesus Silva: Science and Technology Department, University Portucalense, 4200-072 Porto, Portugal

Sustainability, 2022, vol. 14, issue 20, 1-18

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.

Keywords: classification; student success; interpretability; data analysis; higher education institutions; sustainable education (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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