IDEAS home Printed from https://ideas.repec.org/a/taf/edecon/v15y2007i4p405-419.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/10.1080/09645290701409939
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/09645290701409939?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:edecon:v:15:y:2007:i:4:p:405-419. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CEDE20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.