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Students’ Dropout Risk Assessment in Undergraduate Courses of ICT at Residential University – A Case study

by Sweta Rai, Ajit Kumar Jain
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 14
Year of Publication: 2013
Authors: Sweta Rai, Ajit Kumar Jain
10.5120/14645-2965

Sweta Rai, Ajit Kumar Jain . Students’ Dropout Risk Assessment in Undergraduate Courses of ICT at Residential University – A Case study. International Journal of Computer Applications. 84, 14 ( December 2013), 31-36. DOI=10.5120/14645-2965

@article{ 10.5120/14645-2965,
author = { Sweta Rai, Ajit Kumar Jain },
title = { Students’ Dropout Risk Assessment in Undergraduate Courses of ICT at Residential University – A Case study },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 14 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number14/14645-2965/ },
doi = { 10.5120/14645-2965 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:56.087969+05:30
%A Sweta Rai
%A Ajit Kumar Jain
%T Students’ Dropout Risk Assessment in Undergraduate Courses of ICT at Residential University – A Case study
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 14
%P 31-36
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The present case study describes the results of the educational data mining aimed at predicting the undergraduate courses of computer science (BCA and B. Tech. ) students' instant dropout or after first semester. For this purpose, the preliminary data of 220 students collected randomly in prescheduled format on personal interview to find out dropout rates and dropout reasons. The simple and intuitive classifiers (decision trees) ID3 and J48 were used in this paper. The main reason recorded for dropout of students at this residential university were personal factor viz; illness & homesickness, Educational factors viz; learning problems & difficult courses, change of Institution with present goal and low placement rate and institutional factors such as campus environment, too many rules in hostel life and poor entertainment facilities. The information generated will be useful for better planning and implementation of educational program and infrastructure under measurable condition to increase the enrollment rate of students in ICT courses at this university.

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Index Terms

Computer Science
Information Sciences

Keywords

KDD classification dropout educational data mining (EDM) decision tree prediction ICT (Information & Communication Technology)

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