A Review of Recommender Systems for Choosing Elective Courses

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.

@article{Maphosa2020,
  title     = {A Review of Recommender Systems for Choosing Elective Courses},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {9},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Mfowabo Maphosa and Wesley Doorsamy and Babu Paul},
  doi       = {10.14569/IJACSA.2020.0110933},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110933}
}