Volume 5, Issue 6, December 2019, Page: 128-135
A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses
Adewale Opeoluwa Ogunde, Department of Computer Science, Redeemer’s University, Ede, Nigeria
Emmanuel Ajibade, Department of Computer Science, Redeemer’s University, Ede, Nigeria
Received: Oct. 15, 2019;       Accepted: Nov. 14, 2019;       Published: Nov. 21, 2019
DOI: 10.11648/j.ijdsa.20190506.14      View  141      Downloads  55
Abstract
The task of selecting a few elective courses from a variety of available elective courses has been a difficult one for many students over the years. In many higher institutions, guidance and counsellors or level advisers are usually employed to assist the students in picking the right choice of courses. In reality, these counsellors and advisers are most times overloaded with too many students to attend to, and sometimes they do not have enough time for the students. Most times, the academic strength of the student based on past results are not considered in the new choice of electives. Recommender systems implement advanced data analysis techniques to help users find the items of their interest by producing a predicted likeliness score or a list of top recommended items for a given active user. Therefore, in this work, a collaborative filtering-based recommender system that will dynamically recommend elective courses to undergraduate students based on their past grades in related courses was developed. This approach employed the use of the k-nearest Neighbour algorithm to discover hidden relationships between the related courses passed by students in the past and the currently available elective courses. Real-life students’ results dataset was used to build and test the recommendation model. The new model was found to outperform existing results in the literature. The developed system will not only improve the academic performance of students; it will also help reduce the workload on the level advisers and school counsellors.
Keywords
Collaborative Filtering, Elective Undergraduate Courses, K-nearest Neighbour Algorithm, Recommender Systems
To cite this article
Adewale Opeoluwa Ogunde, Emmanuel Ajibade, A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses, International Journal of Data Science and Analysis. Vol. 5, No. 6, 2019, pp. 128-135. doi: 10.11648/j.ijdsa.20190506.14
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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