Recommendation Systems on E-Learning and Social Learning: A Systematic Review
Open accessAbstract
E-learning is renowned as one of the highly effective modalities of learning. Social learning, in turn, is considered to be of major importance as it promotes collaboration between learners. For properly managing learning resources, recommender systems have been implemented in e-learning to enhance learners' experience. Whilst recommender systems are of widespread concern in online learning, it is still unclear to educators how recommender systems can improve the learning process and have a positive impact on learning. This paper seeks to provide an overview of the recommender systems proposed in e-learning between 2007 and the first part of 2021. Out of 100 initially identified publications for the period between 2007 and the first part of 2021, 51 articles were included for final synthesis, according to specific criteria. The descriptive results show that most of the disciplines involved in educational recommender systems papers have approached e-learning in a general way without putting as much emphasis on social learning, and that recommender systems based on explicit feedbacks and ratings were the most frequently used in empirical studies. The synthesis of results presents several recommender systems types in e-learning: (1) Content-based recommender systems, (2) Collaborative-filtering recommender systems, (3) Hybrid recommender systems and (4) Recommender systems based on supervised and unsupervised algorithms. The conclusions reflect on the almost lack of critical reflection on the importance of addressing recommender systems in social learning and social educational networks in particular, especially as social learning has particular requirements, the weak databases size used in some research work, the importance of acknowledging the strengths and weaknesses of each type of recommender system in an educational context and the need for further exploration of implicit feedbacks more than explicit learners’ feedbacks for more accurate recommendations.
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AAs allow for a more accurate measure of the learner's abilities by dynamically changing the difficulty of the questions based on the student's performance evidence.
“These AAs allow for a more accurate measure of the learner's abilities by dynamically changing the difficulty of the questions based on the student's performance evidence [213,214].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [213]
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Cited by
Sorted by citations
Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
“These AAs allow for a more accurate measure of the learner’s abilities by dynamically changing the difficulty of the questions based on the student’s performance evidence [213,214].”
- Authors
- Souabi, S., Retbi, A., Khalidi Idrissi, M. K. I., & Bennani, S.
- Affiliations
- Mohammed V University (MA)
- Corresponding author
- Souabi, Sonia
- Venue
- Electronic Journal of e-Learning
- Published
- 2021-11-12
- Volume
- 19
- Issue
- 5
- Pages
- pp432-451
- ISSN
- 1479-4403 (ISSN-L)
- OpenAlex
- W3213795104
- Cited by
- 27
- References
- 63
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 14 · 2023: 3 · 2022: 3
- FWCI
- 0.76
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- The Electronic Journal of e-Learning (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- recommender systems, online learning, social learning, collaborative filtering, content-based recommendation, hybrid recommendation, implicit feedback, explicit feedback, learning resources, student experience
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