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Early Prediction of Students Performance using Machine Learning Techniques

by Anal Acharya, Devadatta Sinha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 1
Year of Publication: 2014
Authors: Anal Acharya, Devadatta Sinha
10.5120/18717-9939

Anal Acharya, Devadatta Sinha . Early Prediction of Students Performance using Machine Learning Techniques. International Journal of Computer Applications. 107, 1 ( December 2014), 37-43. DOI=10.5120/18717-9939

@article{ 10.5120/18717-9939,
author = { Anal Acharya, Devadatta Sinha },
title = { Early Prediction of Students Performance using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 1 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number1/18717-9939/ },
doi = { 10.5120/18717-9939 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:39:57.153663+05:30
%A Anal Acharya
%A Devadatta Sinha
%T Early Prediction of Students Performance using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 1
%P 37-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years Educational Data Mining (EDM) has emerged as a new field of research due to the development of several statistical approaches to explore data in educational context. One such application of EDM is early prediction of student results. This is necessary in higher education for identifying the "weak" students so that some form of remediation may be organized for them. In this paper a set of attributes are first defined for a group of students majoring in Computer Science in some undergraduate colleges in Kolkata. Since the numbers of attributes are reasonably high, feature selection algorithms are applied on the data set to reduce the number of features. Five classes of Machine Learning Algorithm (MLA) are then applied on this data set and it was found that the best results were obtained with the decision tree class of algorithms. It was also found that the prediction results obtained with this model are comparable with other previously developed models.

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Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining College Education Machine Learning Result Prediction Kappa Statistic F-Measure WEKA.

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