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Educational Data Mining to Predict Bachelors Students’ Success

David Jacob, Roberto Henriques

Abstract


Predicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countries’ economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelor’s historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural network’s best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the student’s previous education and engagement with the school environment are decisive in achieving academic success.

 

Doi: 10.28991/ESJ-2023-SIED2-013

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Keywords


Academic Success; Student Success; Educational Data Mining; Machine Learning.

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Full Text: PDF

DOI: 10.28991/ESJ-2023-SIED2-013

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