The research trends in recommender systems for e-learning: A systematic review of SSCI journal articles from 2014 to 2018

Jiemin ZHONG, Haoran XIE, Fu Lee WANG

Research output: Contribution to journalArticles

Abstract

Purpose - A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.
Design/methodology/approach - The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.
Findings - The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.
Originality/value - The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research. Copyright © 2019 Jiemin Zhong, Haoran Xie and Fu Lee Wang.
Original languageEnglish
Pages (from-to)12-27
JournalAsian Association of Open Universities Journal
Volume14
Issue number1
Early online date18 Jun 2019
DOIs
Publication statusPublished - 2019

Citation

Zhong, J., Xie, H., & Wang, F. L. (2019). The research trends in recommender systems for e-learning: A systematic review of SSCI journal articles from 2014 to 2018. Asian Association of Open Universities Journal, 14(1), 12-27. doi: 10.1108/AAOUJ-03-2019-0015

Keywords

  • Literature review
  • Learning behaviour
  • Assessment of e-learning recommender system
  • Recommendation technology

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