Logistics of Data Mining Techniques in Education, Assessing Academic Performance of Self-Financing Arts and Science College Students
Keywords:Educational data mining, Association Function, Factor Analysis, K-means Cluster Analysis
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 studentsâ€™ 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 Â the academic performance of students studying in self-financing arts and science colleges, data were collected with the information like fatherâ€™s education, motherâ€™s education, classification, subject, college location, facilities, etcÂ from 1398 students through questionnaire. Classification analysis of associated factors with academic performance identified the urban residents, higher parental Â 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,Â faculty concern, classification, Â 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.
Kolo, D.K., S.A. Adepojub and J.K. Alhassanb, â€œA Decision Tree Approach for Predicting Students Academic
Performanceâ€, I. J. Education and Management Engineering, 5, 12-19, 2015.
Mohd Maqsood, A., â€œRole of data mining in education sectorâ€, International Journal of Computer Science and
Mobile Computing, 2,4, 374 â€“ 383, 2013.
Sumathi, S. and S.N. Sivanandam, Introduction to Data Mining And Its Applications. Netherlands, Sringer. ISBN
Nguyen, T.N., L. Drumond, A. Grimberghe and L.S. Thieme, â€œRecommender System for Predicting Student
Performancesâ€, Procedia Computer Science, 1, 2811â€“2819, 2010.
Alaa el-Halees., â€œMining Students Data to Analyze e-Learning Behaviorâ€, A Case Study, 2009.  Baker,R.S.J.D. and K. Yacef, â€œThe State of Educational Data Mining in 2009, A Review and Future Visionsâ€,
Journal of Educational Data Mining, 1,1, 3-16, 2009.
Corbett, A.T., â€œCognitive Computer Tutors, Solving the Two-Sigma Problemâ€, In Proceedings of the International
Conference on User Modeling, 137-147, 2001.
KovaÄiÄ‡, Z., â€œEarly Prediction of Student Success, Mining Students Enrolment Dataâ€, In Proceedings of Informing
Science & IT Education Conference, 647-665, 2010.
Vandamme, J., N. Meskens and J. Super, â€œPredicting Academic Performance by DataMining Methodsâ€, Education
Economics, 15,4, 405-419, 2007.
Kotsiantis, S., C. Pierrakeas and P. Pintelas, â€œPrediction of Studentâ€™s Performance inDistance Learning Using
Machine Learning Techniquesâ€, Applied Artificial Intelligence, 18, 5, 411-426, 2004.
Dekker, G., M. Pechenizkiy and J. Vleeshouwers, â€œPredicting StudentsDrop Out, A Case Studyâ€, In Proceedings of
the International Conference on EducationalData Mining, 2009.
Suman and M. Pooja, â€œA Comparative Study on Role of Data Mining Techniques in Education - A Reviewâ€,
International Journal of Emerging Trends & Technology in Computer Science, 3, 3, 65-69, 2014.
Mihai, A. and C. Daniel, â€œCommercially Available Data Mining Tools used in the Economic Environmentâ€,
Database Systems Journal, 1, 2, 45â€“54, 2010.
Ogor, E. N., and C. Islands, â€œStudent Academic Performance Monitoring and Evaluation Using Data Mining
Techniquesâ€, In, CERMA '07 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference ,
Feng, M., N. Heffernan and K. Koedinger, â€œAddressing the assessment challenge with an online system that tutors
as it assessesâ€, User Modelingand User-Adapted Interaction, 19, 3, 243â€“266, 2009.
Krecar, I.M., M. Kolega and S. F. Kunac, â€œThe Effects of Drinking Water on Attentionâ€, Procedia - Social and
Behavioral Sciences, 159, 577â€“583, 2014.
Ramesh, V., P. Parkavi and K. Ramar, â€œPredicting Studentperformanceâ€, A Statistical and Data Mining Approach,
International Journal of Computer Applications, 63, 8, 36-39, 2013.
Agbaje, O. Rashidat, Awodun and O. Adebisi, â€œImpact of School Location on Academic Achievement of Science
Students in Senior Secondary School Certificate Examinationâ€, International Journal of Scientific and
Research Publications, 4, 9, 1-4, 2014.
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