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Reseach Article

Course Recommendation System

by M. Rekha Sundari, G. Shreya, T. Jawahar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 29
Year of Publication: 2020
Authors: M. Rekha Sundari, G. Shreya, T. Jawahar

M. Rekha Sundari, G. Shreya, T. Jawahar . Course Recommendation System. International Journal of Computer Applications. 175, 29 ( Nov 2020), 13-16. DOI=10.5120/ijca2020920823

@article{ 10.5120/ijca2020920823,
author = { M. Rekha Sundari, G. Shreya, T. Jawahar },
title = { Course Recommendation System },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2020 },
volume = { 175 },
number = { 29 },
month = { Nov },
year = { 2020 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2020920823 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:39:46.573356+05:30
%A M. Rekha Sundari
%A G. Shreya
%A T. Jawahar
%T Course Recommendation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 29
%P 13-16
%D 2020
%I Foundation of Computer Science (FCS), NY, USA

Choice based credit system in higher education employs simple principle of students choosing courses of their interests. This learning platform makes students face difficulty in choosing electives, as the options available are multitudinous. Existing course recommendation systems suggest courses based on either collaborative or content based approach. This work focuses on building an effective Course Recommendation System (CRS) for college students, suggesting the most relevant course based on their learning ability and their preferred choice. In this paper, a rule based approach which addresses the pitfalls and loopholes of the existing technology is suggested. Rule based approach helps to recommend a course better than the existing course recommendation systems.

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Index Terms

Computer Science
Information Sciences


Electives graduation classification personal interest success rate students