Application of clustering algorithm for analysis of Student Academic Performance
DOI:
https://doi.org/10.26438/ijcse/v6i1.381384Keywords:
Academic Performance, Data Mining, Student’s result data, clusteringAbstract
The analysis of the Student academic performance in educational institutions is a crucial task to make managerial decisions and to impart quality education. The data pertaining to the educational institutions is increasing rapidly. Mining these large volumes of the data will help the management to make academia decisions. Predicting the academic performance of the student at an early stage of their course will help the academia to identify the merit students and also to put more efforts in developing remedial programs for the weaker students to improve their performance. In this paper, we applied k-means clustering algorithm for analysing the students result data and predicting the students’ performance.
References
H. Jiawei. Kamber, and J. Pei, Data Mining: Concepts and Techniques, San Francisco California, Morgan Kaufmann Publishers, 2012.
A.K. Jain, Data clustering: “50 years beyond K-means, Pattern Recognition Letters”, Elsevier, vol.31, pp.651-666, 2010.
Md. Hedayetul Islam Shovon, “Prediction of Student Academic Performance by an Application of K-Means Clustering Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2(7), July 2012.
Monika Goyal, Rajan Vohra, “Applications of Data Mining in Higher Education”, International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012
Ayesha, S. , Mustafa, T. , Sattar, A. and Khan, I. (2010) ‗Data Mining Model for Higher Education System‘, European Journal of Scientific Research, vol. 43, no. 1, pp. 24-29.
M. N. Quadri1 and Dr. N.V. Kalyanka- Drop Out Feature of Student Data for Academic Performance Using Decision Tree, Global Journal of Computer Science and Technology Vol. 10 Issue 2 (Ver 1.0), April 2010.
Baradwaj, B. and Pal, S. (2011) ‗Mining Educational Data to Analyze Student s‘ Performance‘, International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, pp. 63-69.
Oyelade, O. J, “Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance”, (IJCSIS) International Journal of Computer Science and Information
Security, Vol.7, 2010.
Sajadin Sembiring, M. Zarlis, Dedy Hartama,Ramliana S, Elvi Wani. Prediction of Student Academic Performance by An Application of Data Mining Techniques, International Conference on Management and Artificial Intelligence, Bali, Indonesia,IPEDR vol.6 ,pp.110-114,2011.
P. Ajith, M.S.S.Sai, B. Tejaswi, Evaluation of Student Performance: An Outlier Detection Perspective, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-2, January, 2013.
D. Kabakchieva, “Analyzing University Data for Determining Student Profiles and Predicting Performance”, Cybernetics and Information Technologies, Vol.1(3), March 2013.
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