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Gaining insight into student satisfaction using comprehensible data mining techniques

Gaining insight into student satisfaction using comprehensible data mining techniques
Gaining insight into student satisfaction using comprehensible data mining techniques
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.
data mining, education evaluation, multi class classification, comprehensibility
0377-2217
548-562
Dejaeger, Karel
491728b5-118c-4661-b003-3787037c8f2d
Goethals, Frank
a25dee80-a572-4310-90a5-9ea8cdc2fc5f
Giangreco, Antonio
66a84288-140b-41e0-85eb-4ba2367d1117
Mola, Lapo
f3abea7b-7469-4ac6-97c7-216970dfe310
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Dejaeger, Karel
491728b5-118c-4661-b003-3787037c8f2d
Goethals, Frank
a25dee80-a572-4310-90a5-9ea8cdc2fc5f
Giangreco, Antonio
66a84288-140b-41e0-85eb-4ba2367d1117
Mola, Lapo
f3abea7b-7469-4ac6-97c7-216970dfe310
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Dejaeger, Karel, Goethals, Frank, Giangreco, Antonio, Mola, Lapo and Baesens, Bart (2012) Gaining insight into student satisfaction using comprehensible data mining techniques. European Journal of Operational Research, 218 (2), 548-562. (doi:10.1016/j.ejor.2011.11.022).

Record type: Article

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.

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More information

Published date: 16 April 2012
Keywords: data mining, education evaluation, multi class classification, comprehensibility
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 336469
URI: http://eprints.soton.ac.uk/id/eprint/336469
ISSN: 0377-2217
PURE UUID: 36c82ef7-e4d4-4949-8947-aaff3fa840c2
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 27 Mar 2012 10:09
Last modified: 10 Dec 2019 01:45

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