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Gaining insight into student satisfaction using comprehensible data mining techniques

Author

Listed:
  • K. Dejeager
  • F. Goethals

    (UMR CNRS 8179 - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

  • A. Giangreco

    (UMR CNRS 8179 - Université de Lille, Sciences et Technologies - 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
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    References listed on IDEAS

    as
    1. 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.
    2. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    3. 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.
    4. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
    5. 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.
    6. 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.
    7. 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.
    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. 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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    3. 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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