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Predicting College Students Dropout using EDM Techniques

by Anjana Pradeep, Jeena Thomas
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 5
Year of Publication: 2015
Authors: Anjana Pradeep, Jeena Thomas
10.5120/ijca2015905328

Anjana Pradeep, Jeena Thomas . Predicting College Students Dropout using EDM Techniques. International Journal of Computer Applications. 123, 5 ( August 2015), 26-34. DOI=10.5120/ijca2015905328

@article{ 10.5120/ijca2015905328,
author = { Anjana Pradeep, Jeena Thomas },
title = { Predicting College Students Dropout using EDM Techniques },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 5 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number5/21957-2015905328/ },
doi = { 10.5120/ijca2015905328 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:12.095446+05:30
%A Anjana Pradeep
%A Jeena Thomas
%T Predicting College Students Dropout using EDM Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 5
%P 26-34
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study examines the factors affecting students’ academic performance that contribute to the prediction of their failure and dropout using educational data mining techniques. This paper suggests the use of various classification techniques to identify the weak students who are likely to perform poorly in their academics. WEKA, an open source data mining tool was used to evaluate the attributes predicting student failure. The data set is comprised of 67 attributes of 150 students who have enrolled in B. Tech Degree Course registered for the academic year 2014-18 in a reputed college in Kerala affiliated to M.G University, Kerala, India. Various classification techniques like induction rules and decision tree have been applied to the data. The results of each of these approaches have been compared to select the one that achieves high accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining (EDM) Classification Dropout WEKA.

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