A Course Recommender System at Higher Education Learning

Abstract

Recommender systems have been employed in entertainment, e-commerce, agriculture, healthcare and education among other industries to provide personalised suggestions to users. Recommender systems help to solve the problem a user being overburdened with information when using online systems. Due to digitization of the course application process in the institutions of higher learning, courses are now made available online in portals for students to apply. These courses are too many for the student to do adequate research before selection. This leads to students being selected to courses that they are not interested in and thus the need for a course recommendation system that suggests a short list of courses that are relevant to the student. This study focussed on developing a knowledge base recommender system prototype for providing personalised course recommendations to students based on their interests and performance. Knowledge based system development life cycle was used to develop the prototype and knowledge acquisition was done from domain experts and documented materials. To identify the interests, a questionnaire is administered. The Hollands three letter Code is then used to identify the personality. The personalities and results are then used to suggest a short list of courses that are relevant to the student. The model developed had an accuracy of 85.12% and thus can be used to recommend courses to students.