Logistics of Data Mining Techniques in Education, Assessing Academic Performance of Self-Financing Arts and Science College Students

R. Senthil Kumar, K. Arulanandam

Abstract


Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. University management focus more on the profile of admitted students, getting aware of the different types and specific students’ characteristics based on the received data. Educational data mining is an emerging field for knowledge discovering from large scale of educational data. To identify the improvement pattern of  the academic performance of students studying in self-financing arts and science colleges, data were collected with the information like father’s education, mother’s education, classification, subject, college location, facilities, etc  from 1398 students through questionnaire. Classification analysis of associated factors with academic performance identified the urban residents, higher parental  education, science students who utilise college facilities and with higher skilled knowledge, time spending, liking college with more faculty concern including good presentation of teaching materials increased the regular students significant with academic performance. The factor analysis identified the four factors,  faculty concern, classification,  location of residence and college and parental education which explained 38.9% of total variation. K-means cluster analysis reduced to five clusters the student data, the first cluster composed with maternal education. Students of the other clusters identified facilities like students cognitive factors. College and home location and finally the subject taken was the fifth cluster.

 


Keywords


Educational data mining, Association Function, Factor Analysis, K-means Cluster Analysis

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