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Data Mining and Management of Higher Education Institutions
Dražena Gašpar
Faculty of Economics, University of Mostar, Mostar, Bosnia and Herzegovina
[email protected]
Mirela Mabić
Faculty of Economics, University of Mostar, Mostar, Bosnia and Herzegovina
[email protected]
The aim of this paper is to investigate the possibilities of applying data mining techniques in
order to enhance decision making processes at the higher education institutions (HEIs).
Today's higher education institutions are faced with large economic, cultural and
demographic challenges that require changes in decision making processes and in the
management of these institutions. For better adapting to these challenges, HEIs need more
knowledge to better do decision making processes. Most of the required knowledge can be
extracted from the data stored in the HEI's databases. Data mining, as a process of extracting
previously unknown and potentially useful and hidden patterns from large databases, should
be helpful in extracting this knowledge. Different techniques and models could be applied like
neural networks, Bayesian networks, association rules, rule-based systems, regression, and
correlation analysis to analyze educational data.
In this paper authors use data mining techniques to analyse and predict which student will
choose which major at Faculty of Economics – University of Mostar in Bosnia and
Herzegovina. Although Faculty formally offers six majors, because of limited resources teachers, classrooms, etc. - it could organize at most four. Because of that, for efficient
management of faculty, it would be valuable if it is possible to predict what major’s students
prefer and to offer these majors.
The main contribution of the paper is an analysis of the data mining applicability in the
process of decision making in HEIs. It also identify if management of HEIs can be enhanced
through data mining technology.
Key words: data mining, higher education institution, management