Data Mining Based on Neural Networks for Education Data Forecasting

Authors

  • Pabreja K PhD., Birla Institute of Technology and Science, Pilani, Rajasthan, India Associate Professor, Maharaja Surajmal Institute (GGSIP University) New Delhi, India

Keywords:

Educational Data Mining, Artificial Neural Network, Back Propagation, Academic Performance, Correlation analysis

Abstract

Now-a-days, data mining has been used extensively in different domains of application for prediction. Data mining has demonstrated promising results in the field of educational prediction. Artificial Neural Networks in particular, find extensive application for understanding the peculiarities of education field but there is still a lot to be done as far as the Indian universities are concerned. In this paper, it has been verified that various personal and academic attributes of students can be used to predict the percentage of marks in graduation, using real data from the students of a Delhi state university’s affiliates.

References

Kotsiantis S.B. and Pintelas P., A decision support prototype tool for predicting student performance in an ODL environment, International Journal of Interactive Technology and Smart Education, 1(4), p.p. 253-263, 2004.

Kotsiantis S.B., Pierrakeas C. and Pintelas P., Predicting students’ performance in distance learning using machine learning techniques, Journal of Applied Artificial Intelligence, 18(5), p.p. 411-426, 2004.

Folorunsho O., Comparative Study of Different Data Mining Techniques Performance in knowledge Discovery from Medical Database, International Journal of Advanced Research in Computer Science and Software Engineering Research Paper, Volume 3, Issue 3, March 2013 ISSN: 2277 128X

Sivanandam S.N., Sumathi S., Deepa S.N.(2009). Introduction to Neural Networks using Matlab, Tata McGraw Hill Education Private Ltd., 2009.

Kosko B.(2005). Neural Networks and Fuzzy Systems, Prentice Hall of India Ltd., 2005.

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Published

2015-01-31

How to Cite

[1]
K. Pabreja, “Data Mining Based on Neural Networks for Education Data Forecasting”, Int. J. Comp. Sci. Eng., vol. 3, no. 1, pp. 33–38, Jan. 2015.

Issue

Vol. 3 No. 1 (2015): IJCSE January Edition

Section

Research Article

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