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Course Recommendation System

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Authors:
M. Rekha Sundari, G. Shreya, T. Jawahar
10.5120/ijca2020920823

Rekha M Sundari, G Shreya and T Jawahar. Course Recommendation System. International Journal of Computer Applications 175(29):13-16, November 2020. BibTeX

@article{10.5120/ijca2020920823,
	author = {M. Rekha Sundari and G. Shreya and T. Jawahar},
	title = {Course Recommendation System},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2020},
	volume = {175},
	number = {29},
	month = {Nov},
	year = {2020},
	issn = {0975-8887},
	pages = {13-16},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume175/number29/31632-2020920823},
	doi = {10.5120/ijca2020920823},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

References

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Keywords

Electives, graduation, classification, personal interest, success rate, students