Predicting Academic Performance by Data Mining Methods
(2007) Education Economics — Vol. 15 (2007)
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- Authors
Meskens, Nadine 
- Abstract
- (en) Academic failure among first-year university students has long fuelled a large number of debates. Many educational psychologists have tried to understand and then explain it. Many statisticians have tried to foresee it. Our research aims to classify, as early in the academic year as possible, students into three groups: the ‘low risk' students, who have a high probability of succeeding, the ‘medium risk' students, who may succeed thanks to the measures taken by the university, and the ‘high risk' students, who have a high probability of failing (or dropping out). This article describes our methodology and provides the most significant variables correlated to academic success among all the questions asked to 533 first-year univer-sity students during the month of November of academic year 2003-04. Finally, it pre-sents the results of the application of discriminant analysis, neural networks, random forests and decision trees aimed at predicting those students' academic success.
- Affiliations
Louvain School of Management Accounting & Finance
Citations
Vandamme, J.-P., Superby Aguirre, J. F., & Meskens, N. (2007). Predicting Academic Performance by Data Mining Methods. Education Economics, 15. https://doi.org/10.1080/09645290701409939 (Original work published 2007)