You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Tuning Data Mining Models to Predict Secondary School Academic Performance
Contributor:
Hoyos, William;
Caicedo-Castro, Isaac
Published:
MDPI AG, 2024
Published in:
Data, 9 (2024) 7, Seite 86
Language:
English
DOI:
10.3390/data9070086
ISSN:
2306-5729
Origination:
Footnote:
Description:
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 F1–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.