Open AccessA review on Recommender Systems for course selection in higher education
Author(s) -
Nathan Lynn,
Andi Wahju Rahardjo Emanuel
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1098/3/032039
Subject(s) - recommender system , selection (genetic algorithm) , task (project management) , computer science , affect (linguistics) , work (physics) , course (navigation) , world wide web , psychology , artificial intelligence , engineering , mechanical engineering , systems engineering , communication , aerospace engineering
Recommender systems are widely used in many fields. These systems work by recommending a personalized list of items to users based on their interests and thus helping users to overcome excessive information offered to them. For users such as students, selecting the right courses is a very challenging task while joining a new academic level. Picking the wrong courses may affect a student’s academic life as well as their future career. This paper aims at exploring the use of recommender systems to assist students in selecting courses that correspond to their abilities and interests. The results from this review showed that the Hybrid recommendation approach/system could be the best method to help students to choose the right courses in preparation for their future careers.