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Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network

by Suchita Borkar, K. Rajeswari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 10
Year of Publication: 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 },
issue_date = { January 2014 },
volume = { 86 },
number = { 10 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number10/15022-3310/ },
doi = { 10.5120/15022-3310 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:03:51.846048+05:30
%A Suchita Borkar
%A K. Rajeswari
%T Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 10
%P 25-29
%D 2014
%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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Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining Apriori algorithm Association Rule Mining Neural network Multi-Layer Perceptron.

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