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Gaining insight into student satisfaction using comprehensible data mining techniques

Author

Listed:
  • K. Dejeager
  • F. Goethals

    (LEM - Lille - Economie et Management - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • A. Giangreco

    (LEM - Lille - Economie et Management - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • L. Mola
  • B. Baesens

Abstract

As a consequence of the heightened competition on the education market, the management of educational institutions often attempts to collect information on what drives student satisfaction by e.g. organizing large scale surveys amongst the student population. Until now, this source of potentially very valuable information remains largely untapped. In this study, we address this issue by investigating the applicability of different data mining techniques to identify the main drivers of student satisfaction in two business education institutions. In the end, the resulting models are to be used by the management to support the strategic decision making process. Hence, the aspect of model comprehensibility is considered to be at least equally important as model performance. It is found that data mining techniques are able to select a surprisingly small number of constructs that require attention in order to manage student satisfaction.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • K. Dejeager & F. Goethals & A. Giangreco & L. Mola & B. Baesens, 2012. "Gaining insight into student satisfaction using comprehensible data mining techniques," Post-Print hal-00787269, HAL.
  • Handle: RePEc:hal:journl:hal-00787269
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00787269
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    References listed on IDEAS

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    1. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    2. A. Giangreco & A. Sebastiano & R. Peccei, 2009. "Trainees' reactions to training: an analysis of the factors affecting overall satisfaction with training," Post-Print hal-00323772, HAL.
    3. Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
    4. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    5. A. Giangreco & A. Carugati & A. Sebastiano & D. Della Bella, 2010. "Trainees' reactions to training : shaping groups and courses for happier trainees," Post-Print hal-00569508, HAL.
    6. A. Giangreco & A. Carugati & A. Sebastiano, 2010. "Are we doing the right thing ? Food for thought on training evaluation and its context," Post-Print hal-00569308, HAL.
    7. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
    8. Altman, Edward I. & Rijken, Herbert A., 2004. "How rating agencies achieve rating stability," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2679-2714, November.
    9. M. Ruth & K. Donaghy & P. Kirshen, 2006. "Introduction," Chapters,in: Regional Climate Change and Variability, chapter 1 Edward Elgar Publishing.
    10. Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
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    1. repec:spr:infosf:v:20:y:2018:i:2:d:10.1007_s10796-016-9704-4 is not listed on IDEAS
    2. Asil Oztekin, 0. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 0, pages 1-16.

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