Gaining insight into student satisfaction using comprehensible data mining techniques

Autor: Lapo Mola, Bart Baesens, Frank Goethals, Antonio Giangreco, Karel Dejaeger
Přispěvatelé: Lille économie management - UMR 9221 (LEM), Université d'Artois (UA)-Université catholique de Lille (UCL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Verona (UNIVR), Università degli studi di Verona = University of Verona (UNIVR), UMR CNRS 8179, Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2012
Předmět:
Information Systems and Management
Knowledge management
General Computer Science
Computer science
Process (engineering)
02 engineering and technology
Management Science and Operations Research
computer.software_genre
Educational evaluation
Industrial and Manufacturing Engineering
Competition (economics)
Order (exchange)
0502 economics and business
0202 electrical engineering
electronic engineering
information engineering
Data mining
ComputingMilieux_MISCELLANEOUS
[SHS.STAT]Humanities and Social Sciences/Methods and statistics
Impact factor
Business education
business.industry
Education evaluation
Multi class classification
Comprehensibility
05 social sciences
Modeling and Simulation
Scale (social sciences)
[SHS.GESTION]Humanities and Social Sciences/Business administration
020201 artificial intelligence & image processing
business
computer
050203 business & management
Zdroj: European Journal of Operational Research
European Journal of Operational Research, Elsevier, 2012, 218 (2), pp.548-562. ⟨10.1016/j.ejor.2011.11.022⟩
European Journal of Operational Research, 2012, 218 (2), pp.548-562. ⟨10.1016/j.ejor.2011.11.022⟩
European Journal of Operational Research, Elsevier, 2012, 218, pp.548-562
ISSN: 0377-2217
1872-6860
Popis: 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.
Databáze: OpenAIRE
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