Tuning Data Mining Models to Predict Secondary School Academic Performance

William Hoyos () and Isaac Caicedo-Castro
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William Hoyos: Sustainable and Intelligent Engineering Research Group, Cooperative University of Colombia, Monteria 230002, Colombia
Isaac Caicedo-Castro: SOCRATES Research Team, Department of Systems and Telecommunications Engineering, Faculty of Engineering, University of Córdoba, Monteria 230002, Colombia

Data, 2024, vol. 9, issue 7, 1-25

Abstract: In recent years, educational data mining has emerged as a growing discipline focused on developing models for predicting academic performance. The primary objective of this research was to tune classification models to predict academic performance in secondary school. The dataset employed for this study encompassed information from 19,545 high school students. We used descriptive statistics to characterise information contained in personal, school, and socioeconomic variables. We implemented two data mining techniques, namely artificial neural networks (ANN) and support vector machines (SVM). Parameter optimisation was conducted through five–fold cross–validation, and model performance was assessed using accuracy and F 1 –Score. The results indicate a functional dependence between predictor variables and academic performance. The algorithms demonstrated an average performance exceeding 80% accuracy. Notably, ANN outperformed SVM in the dataset analysed. This type of methodology could help educational institutions to predict academic underachievement and thus generate strategies to improve students’ academic performance.

Keywords: academic performance; machine learning; data mining; support vector machine; artificial neural networks (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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