Email this article 
Open Access
Subscription or Fee Access
Educational Institutional Quality Assessment Using Cluster and Predictive Data Mining Models
Abstract
The most important facts in educational institutional system growth lies in the quality of services rendered. It concerns with all the circumstances i.e., faculty profile, student performance and infrastructure requirements that allow decision makers to better evaluate and improve the institutional performance. The highest level of quality in educational institution can be achieved by improving the decision making procedures on the various processes such as planning, counseling, evaluation and so on. This can be achieved by utilizing the managerial decision makers with valuable implicit knowledge, which is currently unknown to them. This knowledge is hidden among the educational data set and it is extractable through data mining technology. The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Experimental results show improved stability and accuracy for clustering structures obtained via sub sampling, and adaptive techniques. These improvements offer insights into specific decision within the data sets. The proposal also works in the direction to provide accurate up-to-date and accessible information on performance of education institutions to enable stakeholders to make informed choices. The quality evaluation provides the management with bases for policy options on education and informed decisions for development assistance and incentives to its educational system.
Keywords
References
Aksenova, S. S., Zhang, D. & Lu, M.," Assisting Higher Education in Assessing, Predicting, and Managing Issues Related to Student Success: A Web-based Software using Data Mining and Quality Function Deployment," Academic and Business Research Institute Conference, Las Vegas, 2010.
Bailey, B. Let the data talk: Developing models to explain IPEDS graduation rates. In J. Luan & C. Zhao (Eds.), New Direction for Institutional Research, no. 131. San Francisco: Jossey-Bass, 2006.
Baker, R.S.J.D. 2007. Modeling and Understanding Students’ Off-Task Behavior in Intelligent Tutoring Systems. In Proceedings of the ACM CHI 2007: Computer-Human Interaction conference, 1059-1068.
Baker, R.S.J.D. in press. Data Mining For Education. In International Encyclopedia of Education (3rd edition), B. MCGAW, PETERSON, P., BAKER Ed. Elsevier, Oxford, UK.
Baker R.S.J.D., BARNES, T. and BECK, J.E. 1 st International Conference on Educational Data Mining, Montreal, Quebec, Canada, 2008.
Barnes, T., Desmarais, M., Romero, C. and Ventura, S. Educational Data Mining 2009: 2nd International Conference on Educational Data Mining, Proceedings, Cordoba, Spain, 2009.
Bartneck, C. and Hu, J. Scientometric Analysis of the CHI Proceedings. In Proceedings of the Conference on Human Factors in Computing Systems (CHI2009), 699-708, 2009.
Sujitparapitaya, S.. Considering student mobility in retention outcomes. In J. Luan & C. Zhao (Eds.), New Direction for Institutional Research, no. 131. San Francisco: Jossey-Bass, 2006.
Delavari N, Beikzadeh M. R, Shirazi M. R. A., “A New Model for Using Data Mining in Educational System”, 5th International Conference on Information Technology based Education and Training: ITEHT ’04, Istanbul, Turkey, 31st May-2nd Jun 2004.
Delavari N, Beikzadeh M. R, “A New Analysis Model for Data Mining Processes in Educational Systems”, MMU International Symposium on Information and Communications Technologies 2004 in conjuction with the 5th National Conference on Telecommunication Technology 2004, Putrajaya, Malaysia, 7th- 8th October 2004.
Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C, Wirth R, CRISP-DM 1.0: Step-by-step data mining guide, 2000.
Two Crows Corporation, “Introduction to Data Mining and Knowledge Discovery”, TwoCrows Corporation, Third Edition, U.S.A, 1999.
Han J, Kamber M, ”Data Mining: Concepts and Techniques”, Simon Fraser University, Morgan Kaufmann publishers, ISBN 1-55860-489-8. 2001.
Chen M .S, Han J, Yu P. S, "Data Mining: An Overview from a Database Perspective". IEEE Transaction on Knowledge and Data Engineering, 1996.
Han J, "How can Data Mining Help Bio-Data Analysis". BIOKDD02: Workshop on data mining in Bioinformatics, 2002.
Feldman R, "Mining the Biomedical Literature using Semantic Analysis and Neural Language Processing Techniques, a link analysis approaches". ClearForest Corporation, New York, 2003.
Edelstein H, "Building Profitable Customer Relationships with Data Mining", Two Crows Corporation, SPSS white paper-executive briefing, 2000.
Chang W. H. T, Lee Y. H, " Telecommunications Data Mining for Target Marketing," Journal of Computers, Vol. 12, No. 4, December 2000, pp.60-74.
Mobasher B, Jain N, Han E, Srivastava J, "Web Mining: Pattern Discovery from World Wide Web Transactions", Technical Report TR96-050, Department of Computer Science, University of Minnesota, 1996.
The Y. W, Mustaffa K. M, Zaitun A. B, Lee, "Data Mining In Computer Auditing". Informing Science. Cork, Ireland June 19-21, 2002.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.