Version 1
: Received: 20 December 2023 / Approved: 21 December 2023 / Online: 21 December 2023 (16:04:51 CET)
Version 2
: Received: 27 December 2023 / Approved: 28 December 2023 / Online: 28 December 2023 (15:19:04 CET)
How to cite:
Maryam, O.; Ashraf, H.; Amjad, T.; Jhanjhi, N. Improved E-learning Based Recommender Systems: A Survey. Preprints2023, 2023121672. https://doi.org/10.20944/preprints202312.1672.v2
Maryam, O.; Ashraf, H.; Amjad, T.; Jhanjhi, N. Improved E-learning Based Recommender Systems: A Survey. Preprints 2023, 2023121672. https://doi.org/10.20944/preprints202312.1672.v2
Maryam, O.; Ashraf, H.; Amjad, T.; Jhanjhi, N. Improved E-learning Based Recommender Systems: A Survey. Preprints2023, 2023121672. https://doi.org/10.20944/preprints202312.1672.v2
APA Style
Maryam, O., Ashraf, H., Amjad, T., & Jhanjhi, N. (2023). Improved E-learning Based Recommender Systems: A Survey. Preprints. https://doi.org/10.20944/preprints202312.1672.v2
Chicago/Turabian Style
Maryam, O., Tehmina Amjad and Noor Jhanjhi. 2023 "Improved E-learning Based Recommender Systems: A Survey" Preprints. https://doi.org/10.20944/preprints202312.1672.v2
Abstract
Online learning is gaining massive popularity with time. The e-learning platforms operate differently from traditional educational institutions and hence need different strategy for course recommendations. This survey aims to cover the major emerging research areas in e-learning recommender systems. Our study covers different areas of research including graph-based methodologies, ITS, query optimization, content-based, and collaborative filtering, big data, and association rules mining. This survey aimed to explore all major emerging directions of recommender systems in education. This study analyzed existing literature in all of the areas mentioned before and performed objective-based analysis. A brief performance analysis was also done for the researches where the values were comparable. Limitations of existing researches were also identified and studied to shed some light on future directions.
Keywords
E-learning; Learning Analytics; Machine Learning; MOOCs; Recommender Systems
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
28 December 2023
Commenter:
Noor Jhanjhi
Commenter's Conflict of Interests:
Author
Comment:
A new section and contents have been added along with the Figure as well, such as Figure number 4. In addition to this, a new contributing author has been added. Kindly proceed with this updated version. Thank you in advance. Prof. Dr NZ Jhanjhi
Commenter: Noor Jhanjhi
Commenter's Conflict of Interests: Author
Thank you in advance.
Prof. Dr NZ Jhanjhi