Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/4507
Title: Interpretable success prediction in higher education institutions using pedagogical surveys
Authors: Leal, Fátima
Veloso, Bruno
Santos-Pereira, Carla
Moreira, Fernando
Durão, Natércia
Jesus-Silva, Natacha
Keywords: Classification
Student success
Interpretability
Data analysis
Higher education institutions
Sustainable education
Issue Date: 18-Oct-2022
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Citation: Leal, F., Veloso, B., Santos-Pereira, C., Moreira, F., Durão, N., Jesus-Silva, N. (2022). Interpretable success prediction in higher education institutions using pedagogical surveys. Sustainability, 14, 13446, 1-18. https://doi.org/10.3390/su142013446. Repositório Institucional UPT. http://hdl.handle.net/11328/4507
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.
URI: http://hdl.handle.net/11328/4507
ISSN: 2071-1050
Appears in Collections:REMIT – Artigos em Revistas Internacionais / Papers in International Journals



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