Title: Gaining insight into student satisfaction using comprehensible data mining techniques
Authors: Dejaeger, Karel ×
Goethals, Frank
Giangreco, Antonio
Mola, Lapo
Baesens, Bart #
Issue Date: 2012
Publisher: Elsevier
Series Title: European Journal of Operational Research vol:218 issue:2 pages:548-562
Article number: 562
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.
ISSN: 0377-2217
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven
Faculty of Business and Economics, Campus Kulak Kortrijk – miscellaneous
× corresponding author
# (joint) last author

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