A review of recommender systems for choosing elective courses

Mfowabo Maphosa, Wesley Doorsamy, Babu Paul

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)287-295
Number of pages9
JournalInternational Journal of Advanced Computer Science and Applications
Volume11
Issue number9
DOIs
Publication statusPublished - 2020

Keywords

  • Data mining algorithms
  • Elective courses
  • Higher education
  • Recommender systems
  • Systematic literature review

ASJC Scopus subject areas

  • General Computer Science

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