Research Article
Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 86 - Issue 10 |
| Published: January 2014 |
| Authors: Suchita Borkar, K. Rajeswari |
10.5120/15022-3310
|
Suchita Borkar, K. Rajeswari . Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network. International Journal of Computer Applications. 86, 10 (January 2014), 25-29. DOI=10.5120/15022-3310
@article{ 10.5120/15022-3310,
author = { Suchita Borkar,K. Rajeswari },
title = { Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 86 },
number = { 10 },
pages = { 25-29 },
doi = { 10.5120/15022-3310 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Suchita Borkar
%A K. Rajeswari
%T Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network%T
%J International Journal of Computer Applications
%V 86
%N 10
%P 25-29
%R 10.5120/15022-3310
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Education Data mining plays an important role in predicting students' performance,. It is a very promising discipline which has an imperative impact. In this paper students' performance is evaluated and some attributes are selected which generate rules by means of association rule mining. . Artificial neural network checks accuracy of the results. A Multi-Layer Perceptron Neural Network is employed for selection of interesting features using 10 – fold cross validation. The artificial neural network selects 5 out of 8 attributes based on the accuracy obtained for correctly classified data. It is observed that in association rule mining important rules are generated using these selected attributes. The Experiment is conducted using Weka and real time data set available in the college premises.
References
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