Volume 8, Issue 1, February 2019, Page: 12-17
A Framework for Adaptive Personalized E-learning Recommender Systems
Karim Moharm, Electrical Engineering Department, Alexandria University, Alexandria, Egypt
Received: Jan. 6, 2019;       Accepted: Mar. 6, 2019;       Published: Mar. 25, 2019
DOI: 10.11648/j.ijiis.20190801.13      View  397      Downloads  55
With the undergoing technological revolution in education, adapting recommender systems to the personalized e-learning is an emerging topic in the education sector. Detecting the student model offers a potential to recommend a learning material that is adequate to the student progress. Accordingly, the learning objects and hypermedia can be adapted to each individual student to meet the personalized learning needs. This paper proposes a framework for applying recommender systems in personalized e-learning domain. Furthermore, the recommender system previous examples, opportunities, and associated challenges are discussed.
E-Learning, Recommender, Personalized
To cite this article
Karim Moharm, A Framework for Adaptive Personalized E-learning Recommender Systems, International Journal of Intelligent Information Systems. Vol. 8, No. 1, 2019, pp. 12-17. doi: 10.11648/j.ijiis.20190801.13
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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