CLUSTER ANALYSIS OF SSTS FRAMEWORK USING SOCIAL NETWORK ANALYSIS
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 6
Abstract
Educational Data Mining (EDM) is an emerging trend, concerned with developing methods for exploring the large data related to educational system. The data is used to obtain the Implicit and Explicit knowledge distribution. Educational Data mining methods are generally used to determine the performance of students, assessment of students and study students’ behavior etc. In recent years, Educational data mining has proven to be more successful at many of the educational statistics problems due to enormous computing power and data mining algorithms [1]. This research explores the applications of data mining techniques applied in educational field. The work explores SSTS, a new teaching learning process framework in higher education. The framework contains students database consists of various relational attributes. By applying data mining techniques to the database and mined the effective students’ knowledge distribution clusters using social network analysis. The analysis found that good student faculty relation network clusters and average percentile of effective knowledge distribution by students’ in higher education.
Authors and Affiliations
M. Sindhuja , Dr. S. Rajalakshmi , S. T. Tharani
Keywords
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