Analysis of Student’s Data Using Rapid Miner

  • Sheena Angra Ph.D Scholar, Chitkara University, Punjab, India
Keywords: Educational Data Mining, Data Mining, EDM Objectives, Rapid Miner, EDM data and Stakeholders

Abstract

Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey..

Downloads

Download data is not yet available.

References

[1] Sachin R., Vijay M., “A survey and Future Vision of Data mining in Educational Field”, In Second International Conference on Advanced Computing & Communication Technologies, 2012.
[2] Jindal and Dutta Borah, “A Survey on Educational Data Mining and ResearchTrends”, In International Journal of Database Management Systems, 2013, Vol. 5, No. 3.
[3] Prakash, Hanumanthappa & Kavitha, “Big Data in Educational Data Mining and Learning Analytics”, In International Journal of Innovative Research in Computer and Communication Engineering, 2014, Vol. 2.
[4] Romero, C., and Ventura S., “Data Mining in Education, WIREs Data Mining and Know. Dis., 2013, Vol. 3, pp.12–27.
[5] Ha, S., Bae, S., and Park, S., “Web mining for distance education,” In Proc.Int. Conf. On Management of Innovation and Technology, IEEE., 2000, pp. 715–719.
[6] Romero, C., and Ventura, S., “Educational Data Mining : A survey from 1995 to 005”, Expert Systems with Applications., 2007, Vol. 33, pp. 135–146.
[7] Rangra and Bansal., “Comparative Study of Data Mining Tools”, International Journal of Advanced Research in Computer Science and Software Engineering 4(6), June - 2014, pp. 216–223.
[8] U.S. Department of Education, Office of Educational Technology. “Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief” (PDF). Retrieved 30 March 2014.
[9] Assessing the Economic Impact of Copyright Reform in the Area of Technology-Enhanced Learning. Retrieved 6 April 2014.
[10] Azarnoush, Bahareh, et al. “Toward a Framework forLearner Segmentation”. JEDMJournal of Educational Data Mining 5.2, 2013: 102–126.
[11] Huebner, Richard A. “A survey of educational datamining research (PDF)”. Research in Higher Education Journal. Retrieved 30 March 2014.
[12] Jain, A. K., Murty, M. N., & Flynn, P. J., “Data clustering: A review”, ACM Computing Surveys, 31(3), 1999, (pp. 264–323).
[13] Yedla, Pathakota, & Srinivasa, Enhancing K-Means Clustering Algorithm with Improved Initial Center, International Journal of Computer Science and Information Technologies,2010, Vol. 1 (2), 121-125.
[14] Romero, C. &Ventura, S., “Educational Data Mining: A Review of the State-of-the-Art, Systems, Man, and Cybernetics” Part C: Applications and Reviews, IEEE Transactions on 40.6(2010):601-618.
[15] Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler,T. “YALE: Rapid Prototyping for Complex Data Mining tasks”, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-06), pp. 935–940, 2006.
[16] Ralf Mikut and Markus Reischl Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1( 5), pp. 431–443, September/October 2011
Published
2016-06-07
How to Cite
Sheena Angra. (2016). Analysis of Student’s Data Using Rapid Miner. Journal on Today’s Ideas - Tomorrow’s Technologies, 4(1), 49-58. https://doi.org/10.15415/jotitt.2016.41004
Section
Articles