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Predicting Academic Performance by Data Mining Methods


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


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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    References listed on IDEAS

    1. Philip Andrew Stevens, 2005. "A Stochastic Frontier Analysis of English and Welsh Universities," Education Economics, Taylor & Francis Journals, vol. 13(4), pages 355-374.
    2. Izadi, Hooshang & Johnes, Geraint & Oskrochi, Reza & Crouchley, Robert, 2002. "Stochastic frontier estimation of a CES cost function: the case of higher education in Britain," Economics of Education Review, Elsevier, vol. 21(1), pages 63-71, February.
    3. Johnes, Geraint & Johnes, Jill, 2009. "Higher education institutions' costs and efficiency: Taking the decomposition a further step," Economics of Education Review, Elsevier, vol. 28(1), pages 107-113, February.
    4. Cohn, Elchanan & Rhine, Sherrie L W & Santos, Maria C, 1989. "Institutions of Higher Education as Multi-product Firms: Economies of Scale and Scope," The Review of Economics and Statistics, MIT Press, vol. 71(2), pages 284-290, May.
    5. Johnes, Geraint, 1997. "Costs and Industrial Structure in Contemporary British Higher Education," Economic Journal, Royal Economic Society, vol. 107(442), pages 727-737, May.
    6. Glass, J C & McKillop, Donal G & Hyndman, N, 1995. "Efficiency in the Provision of University Teaching and Research: An Empirical Analysis of UK Universities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(1), pages 61-72, Jan.-Marc.
    7. de Groot, Hans & McMahon, Walter W & Volkwein, J Fredericks, 1991. "The Cost Structure of American Research Universities," The Review of Economics and Statistics, MIT Press, vol. 73(3), pages 424-431, August.
    8. Geraint Johnes, 1996. "Multi-product cost functions and the funding of tuition in UK universities," Applied Economics Letters, Taylor & Francis Journals, vol. 3(9), pages 557-561.
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