Mining Association Rules from Empirical Data in the Domain of Education
Author(s) -
Dragica Radosav,
Eleonora Brtka,
Vladimir Brtka
Publication year - 2014
Publication title -
international journal of computers communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2012.5.1352
Subject(s) - computer science , domain (mathematical analysis) , association rule learning , data mining , process (computing) , variety (cybernetics) , data science , point (geometry) , software , data stream mining , artificial intelligence , mathematical analysis , geometry , mathematics , programming language , operating system
The data mining techniques and their applications are widely recognized as powerful tools in various domains. In the domain of education there is a variety of data of various types that are collected. The important question is: Is it possible to process collected data with the data mining technique and what are main advantages of data mining and e-learning interaction? If an e-learning system accumulates a huge volume of data, then it is possible to deploy techniques and tools from the domain of data mining in order to gain valuable information. The research presented in this paper is conducted on real-life data that originates from the Balkan region. The software system Weka is used to generate association rules. The main result of this research is the assessment of the parameters that are associated with the opinion that computer skills will be helpful in the future, from the students point of view. This result is very important because it gives the exact insight to computer technology usage in the Balkans schools. Furthermore, some advantages of the usage of data mining techniques in the domain of education are determined.
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