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Application of Feature Selection Methods in Educational Data Mining

by Anal Acharya, Devadatta Sinha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 2
Year of Publication: 2014
Authors: Anal Acharya, Devadatta Sinha
10.5120/18048-8951

Anal Acharya, Devadatta Sinha . Application of Feature Selection Methods in Educational Data Mining. International Journal of Computer Applications. 103, 2 ( October 2014), 34-38. DOI=10.5120/18048-8951

@article{ 10.5120/18048-8951,
author = { Anal Acharya, Devadatta Sinha },
title = { Application of Feature Selection Methods in Educational Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 2 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number2/18048-8951/ },
doi = { 10.5120/18048-8951 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:32.330940+05:30
%A Anal Acharya
%A Devadatta Sinha
%T Application of Feature Selection Methods in Educational Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 2
%P 34-38
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the recent years, web based learning has emerged as a new field of research due to growth of network and communication technology. These learning systems generate a large volume of student data. Data mining algorithms may be applied on this data set to study interesting patterns. As an example, student enrollment data and his past examination records could be used to predict his grades in the term end examination. However this prediction could mean examining a lot of features of the student data resulting in creation of a model with high computational complexity. In this context this work first defines a student data set with 309 records and 14 features collected by a survey from various graduation level students majoring in Computer Science under University of Calcutta. Different feature selection algorithms are applied on this data set. The best results are obtained by Correlation Based Feature Selection algorithm with 8 features. Subsequently classification algorithms may be applied on this feature subset for predicting student grades.

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Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining (EDM) Kappa Statistic F-measure Prediction Accuracy College Education WEKA.

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