(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 9, 2020 287 | Page www.ijacsa.thesai.org A Review of Recommender Systems for Choosing Elective Courses Mfowabo Maphosa 1 , Wesley Doorsamy 2 , Babu Paul 3 Institute for Intelligent Systems, University of Johannesburg Johannesburg, South Africa Abstract—In higher education, students face challenges when choosing elective courses in their study programmes. Most higher education institutions employ advisors to assist with this task. Recommender systems have their origins in commerce and are used in other sectors such as education. Recommender systems offer an alternative to the use of human advisors. This paper aims to examine the scope of recommender systems that assist students in choosing elective courses. To achieve this, a systematic literature review (SLR) on recommender systems corpus for choosing elective courses published from 2010–2019 was conducted. Of the 16 981 research articles initially identified, only 24 addressed recommender systems for choosing elective courses and were included in the final analysis. These articles show that several recommender systems approaches and data mining algorithms are used to achieve the task of recommending elective courses. This study identified gaps in current research on the use of recommender systems for choosing elective courses. Further work in several unexplored areas could be examined to enhance the effectiveness of recommender systems for elective courses. This study contributes to the body of literature on recommender systems, in particular those applied for assisting students in choosing elective courses within higher education. Keywords—Recommender systems; elective courses; data mining algorithms; systematic literature review; higher education I. INTRODUCTION Looking through the current lens, in and post COVID-19, it is clear that higher education institutions (HEIs) have to change the way they engage with students from the traditional methods to an online or a blended approach. Popenici and Kerr [1] propose that it is time for HEIs to reimagine their function and pedagogical models in a new paradigm with technology at the centre. This calls for increased application and adaptation of artificial intelligence, machine learning and data mining tools to equip the education sector [2]. Many degree programmes offer elective courses in addition to compulsory ones. The courses that students fail to complete include both compulsory and elective courses. Students chose by a student elective courses based on their interests. Predicting student grades in the courses, they will enroll for is useful for guiding students and allowing them to make informed choices regarding compulsory, and elective courses [3]. In higher education, students are faced with difficulties when choosing elective courses. A survey of first-year students at the University College Dublin showed that almost half of the students selected elective courses outside their major because they perceived the courses to be exciting. Some of the difficulties emanate from the limited capacity in some elective courses as well as timetable clashes with compulsory courses which make students choose other elective courses [4]. Finding the most suitable elective course from the available ones can be achieved by using recommender systems [5]. By analysing data on the courses that students completed, it is possible to categorize a student’s interests. The ability to predict student enrolment patterns for courses provides an opportunity for HEIs to be effective in allocating resources and providing a high-quality learning experience [6]. Predicting student grades in future courses before they take them is an essential tool that can be used to assist students with choosing elective courses [3]. The purpose of recommender systems is to recommend a product to a user that would possibly interest them based on the user profile [7]. A typical recommender system uses three elements: a user, item and rating. The recommender system attempts to predict a rating that a particular user would provide for unrated items [8]. Recommender systems use different types of input data which are placed in a matrix with one dimension representing users and the other one items of interest [9]. The rest of this paper is organised as follows: Section 2 provides a brief literature review; Section 3 discusses the methodology that was followed for the study; Section 4 presents the findings of the study and proposes work that needs to be considered in the field of recommender systems for choosing elective courses; Section 5 discusses the implications of the findings and suggests new trends in the field that can enhance recommender systems and Section 6 summarises the paper. II. LITERATURE REVIEW Laghari [10] warns that poor course selection can cause delays in completing a qualification because students have not completed prerequisite courses, or they have missed the minimum credit requirements for the qualification. Selecting the right elective course is vital for the student to complete their degree programme [11]. Choosing an elective course is influenced by several factors such as the student’s personal and academic interest as well as institutional regulations that govern when a particular elective course can be enrolled for [6].