Knowledge Discovery in Higher Educational Big Dataset

Knowledge Discovery in Higher Educational Big Dataset

Robab Saadatdoost (Department of Information Systems, Faculty of Computing, Universiti Teknologi Malaysia,Johor Bahru, Johor, Malaysia & Department of Computer and Information Technology, Parand Branch, Islamic Azad University, Parand, Iran), Alex Tze Hiang Sim (Department of Information Systems, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia), Jee Mei Hee (Department of Educational Foundation, Faculty of Education, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia) and Hosein Jafarkarimi (Department of Computer, Damavand Branch, Islamic Azad University, Damavand, Iran)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/ijirr.2013010104
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Abstract

This paper seeks to address one of the current issues of large organizations; it is rapid growth of data without any quick way to extract worthwhile and hidden knowledge from considerable huge volume of data. It seems that management of higher education institutes interest to the best method for solving this problem and making a good decisions and strategies. Contrast to the authors’ initial sample consisted of five medical universities of Tehran, in this paper a large sample was chosen because of the expected valuable discovered knowledge. This data was collected from 65 universities of Iran based 18 years. The data is in Persian. The present paper confirms the authors’ previous findings and contributes additional discovered knowledge related to the major group of program with different geographical, the main factor of sharp increase in the number of students and preferred learning style, study mode and programs and considerable growth of female students after 1996. The findings of this study have a number of important implications for future planning of higher education to improve ranking of universities. Another important practical implication is that other researchers can use them in their studies on higher education.
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Literature Review

In recent years, there has been a limited literature on data mining in education. Romero did a survey from 1995 to 2005 on educational data mining (Romero & Ventura, 2007). He reported data can come from two types of educational systems: Traditional classroom and distance education (Romero & Ventura, 2007). Besides he pointed educational data mining is an immature research area and it is essential more specialized work.

To determine the use of data mining in educational environment, Vranic, Pintar, et al (2007) presented how data mining techniques can be used in the academic environment to develop some aspects of education quality.

In 2009 Vialardi did a research on the use of data mining techniques for recommendation system used by students for decision making on their academic programs. The main point of this research is on extracting knowledge form students’ performance (Vialardi, Bravo, Shafti, & Ortigosa, 2009). Their work contains data preprocessing and pattern extraction and evaluation to discover patterns that can be used for recommendation systems intended for students.

In other case, Baepler and Murdoch pointed how data mining techniques and their results can be useful to those who are in the education domain (Baepler & Murdoch, 2010). They reported many historians have argued that common data mining techniques which used in higher education are clustering, classification, visualization, and association analysis (Baepler & Murdoch, 2010). Their work is largely expletory and focuses more on the prospective of these analyses.

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