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Gaining insight into student satisfaction using comprehensible data mining techniques

Author

Listed:
  • Karel Dejaeger
  • Frank Goethals

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Antonio Giangreco

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Lapo Mola

    (UNIVR - Università degli studi di Verona = University of Verona)

  • Bart 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

  • Karel Dejaeger & Frank Goethals & Antonio Giangreco & Lapo Mola & Bart Baesens, 2012. "Gaining insight into student satisfaction using comprehensible data mining techniques," Post-Print halshs-01929190, HAL.
    • Handle: RePEc:hal:journl:halshs-01929190
    DOI: 10.1016/j.ejor.2011.11.022
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    Cited by:

    1. Asil Oztekin, 2018. "Creating a marketing strategy in healthcare industry: a holistic data analytic approach," Annals of Operations Research, Springer, vol. 270(1), pages 361-382, November.
    2. Asil Oztekin, 0. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    3. Asil Oztekin, 2018. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 20(2), pages 223-238, April.
    4. Benoit, Dries F. & Tsang, Wai Kit & Coussement, Kristof & Raes, Annelies, 2024. "High-stake student drop-out prediction using hidden Markov models in fully asynchronous subscription-based MOOCs," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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