A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses
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Date
2019
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Science Publishing Group
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.