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Predicting Academic Performance by Data Mining Methods

Author

Listed:
  • J. -P. Vandamme
  • N. Meskens
  • J. -F. Superby

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.

Suggested Citation

  • J. -P. Vandamme & N. Meskens & J. -F. Superby, 2007. "Predicting Academic Performance by Data Mining Methods," Education Economics, Taylor & Francis Journals, vol. 15(4), pages 405-419.
    • Handle: RePEc:taf:edecon:v:15:y:2007:i:4:p:405-419
    DOI: 10.1080/09645290701409939
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    Citations

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    Cited by:

    1. Annalina Sarra & Lara Fontanella & Simone Zio, 2019. "Identifying Students at Risk of Academic Failure Within the Educational Data Mining Framework," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 41-60, November.
    2. Rupert G. Rhodd & Sandra M. Schrouder & Marcus T. Allen, 2009. "Does the Performance on Principles of Economics Courses Affect the Overall Academic Success of Undergraduate Business Majors?," International Review of Economic Education, Economics Network, University of Bristol, vol. 8(1), pages 48-63.
    3. Behr Andreas & Giese Marco & Teguim K Herve D. & Theune Katja, 2020. "Early Prediction of University Dropouts – A Random Forest Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(6), pages 743-789, December.
    4. Abdelgader Alamrouni & Fidan Aslanova & Sagiru Mati & Hamza Sabo Maccido & Afaf. A. Jibril & A. G. Usman & S. I. Abba, 2022. "Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach," IJERPH, MDPI, vol. 19(2), pages 1-22, January.
    5. Cindi Mason & Janet Twomey & David Wright & Lawrence Whitman, 2018. "Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(3), pages 382-400, May.
    6. Murat Gunduz & Hamza M. A. Lutfi, 2021. "Go/No-Go Decision Model for Owners Using Exhaustive CHAID and QUEST Decision Tree Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    7. Iván Sandoval-Palis & David Naranjo & Jack Vidal & Raquel Gilar-Corbi, 2020. "Early Dropout Prediction Model: A Case Study of University Leveling Course Students," Sustainability, MDPI, vol. 12(22), pages 1-17, November.

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