Open Access Open Access  Restricted Access Subscription or Fee Access

Applications of Data Mining Techniques in Higher Education

B. Shathya

Abstract


Data mining (DM) is useful for collecting and interpreting significant data from huge database. The education field offers several potential data sources for data mining applications. These applications can help both instructors and students in improving the learning process. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large datasets. This paper addresses the applications of data mining in educational institution to extract useful information from the huge data sets and providing analytical tool to view and use this information for decision making processes by taking real life examples.


Keywords


Data Mining

Full Text:

PDF

References


Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press.

Han, J. W., Kamber, M., 2006. Data Mining: Concepts and Techniques, 2nd Edition, The Morgan Kaufmann Series in Data Management Systems, Gray, J. Series Editor, Morgan Kaufmann Publishers.

Harry, Z., 2004. The Optimality of Naive Bayes, FLAIRS2004 conference.

Herzog, S., 2006. Estimating student retention and degreecompletion time: Decision trees and neural networks vis-a-vis regression, New Directions for Institutional Research, p.17-33.

Luan, J., 2002. Data mining and knowledge management in higher education – potential applications. In Proceedings of AIR Forum, Toronto, Canada.

Mazon, J. N., Trujillo, J., Serrano, M., Piattini, M., 2005. Applying MDA to the development of data warehouses. DOLAP 2005

Minaei-Bidgoli, B., Kortemeyer, G., Punch,W.F., 2004. Enhancing Online Learning Performance: An Application of Data Mining Methods, In Proceeding of Computers and Advanced Technology in Education.

Oussena, S., 2008. Mining Courses Management Systems, Thames Valley University.

Schönbrunn, K., Hilbert, A., 2006. Data Mining in Higher Education, Studies in Classification, Data Analysis, Westphal, C., Blaxton, T., 1998. Data Mining Solutions, John Wiley.

Witten, I. H., Frank, E., 2005. Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco.

Yorke, M., Longden, B., 2004. Retention and student success in higher education.Society for Research in Higher Education.

Luan, J. (2002b), Data Mining Application in Higher Education, SPSS Executive Report.

Luan, J., Zhao, C.M. and Hayek, J. (2004). Use data mining techniques to develop institutional typologies for NSSE, National Survey of Student Engagement.

Modern Data Warehousing, Mining and Visualization Core Concepts, George M. Marakas.

Wako, T. N. (2003), Basic Indicators of Educational System’s Performance, National Educational Statistics Information Systems, UNESCO, Harare, Zimbabawe.

Yang, M., Goldstein, H., Rath, T. and Hill, N. (1999), The use of assessment data for school improvement purposes, Oxford Review of Education, 25, 469–483.

Zhang Yofeng, Wu Jinhong, Wang Cuibo, Automatic Competitive Intelligence Collection Based On Semantic web mining IEEE 2007

Wang Jain, Li Zhuo, Research and Realization of Long Distance Education platform based on Web Mining, IEEE 2009


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.