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Predicting Academic Performance by Data Mining Methods

Predicting Academic Performance by Data Mining Methods Abstract 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 university students during November of academic year 2003/04. Finally, it presents the results of the application of discriminant analysis, neural networks, random forests and decision trees aimed at predicting those students' academic success. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Education Economics Taylor & Francis

Predicting Academic Performance by Data Mining Methods

Education Economics , Volume 15 (4): 15 – Dec 1, 2007
15 pages

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References (25)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1469-5782
eISSN
0964-5292
DOI
10.1080/09645290701409939
Publisher site
See Article on Publisher Site

Abstract

Abstract 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 university students during November of academic year 2003/04. Finally, it presents the results of the application of discriminant analysis, neural networks, random forests and decision trees aimed at predicting those students' academic success.

Journal

Education EconomicsTaylor & Francis

Published: Dec 1, 2007

Keywords: Academic performance; decision trees; random forests; neural networks; discriminant analysis; education; prediction

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