Comparison of Different Classification Techniques for Educational Data

Comparison of Different Classification Techniques for Educational Data

Kavita Pabreja (Department of Computer Science, Maharaja Surajmal Institute (an affiliate of GGSIP University), New Delhi, India)
DOI: 10.4018/IJISSS.2017010104


Data mining has been used extensively in various domains of application for prediction or classification. Data mining improves the productivity of its analysts tremendously by transforming their voluminous, unmanageable and prone to ignorable information into usable pieces of knowledge and has witnessed a great acceptance in scientific, bioinformatics and business domains. However, for education field there is still a lot to be done, especially there is plentiful research to be done as far as Indian Universities are concerned. Educational Data Mining is a promising discipline, concerned with developing techniques for exploring the unique types of educational data and using those techniques to better understand students' strengths and weaknesses. In this paper, the educational database of students undergoing higher education has been mined and various classification techniques have been compared so as to investigate the students' placement in software organizations, using real data from the students of a Delhi state university's affiliates.
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Literature Review

Educational Data Mining (EDM) is a relatively new stream in the data mining research and there are only a few studies by researchers in this field. Extensive literature reviews of the EDM research field have been done by Romero & Ventura (2007), covering the research efforts in the area, between 1995 and 2005, and by Baker & Yacef (2009), for the period after 2005. Romero & Ventura surveyed the application of data mining to traditional educational systems, particularly web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. They have applied data mining techniques viz. statistics and visualization; clustering, classification and outlier detection; association rule mining; pattern mining and text mining.

The potential applications of data mining in higher education have been explained by Luan in his work in 2002. The author has also discussed how data mining saves resources while maximizing efficiency in academics. Ma, Liu, Wong, Yu, & Lee (2000) focussed on understanding student types and then opting for targeted marketing by applying different data mining models. Tair & El-Halees (2012) have used educational data mining to improve graduate students’ performance, and to overcome the problem of low grades of graduate students. The authors have tried to extract useful knowledge from graduate students data collected from the college of Science and Technology for a period of fifteen years [1993-2007].

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