International Journal of Intelligent Information Systems

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A Framework for Adaptive Personalized E-learning Recommender Systems

Received: 06 January 2019    Accepted: 06 March 2019    Published: 25 March 2019
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Abstract

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.

DOI 10.11648/j.ijiis.20190801.13
Published in International Journal of Intelligent Information Systems (Volume 8, Issue 1, February 2019)
Page(s) 12-17
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

E-Learning, Recommender, Personalized

References
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Author Information
  • Electrical Engineering Department, Alexandria University, Alexandria, Egypt

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    Karim Moharm. (2019). A Framework for Adaptive Personalized E-learning Recommender Systems. International Journal of Intelligent Information Systems, 8(1), 12-17. https://doi.org/10.11648/j.ijiis.20190801.13

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    Karim Moharm. A Framework for Adaptive Personalized E-learning Recommender Systems. Int. J. Intell. Inf. Syst. 2019, 8(1), 12-17. doi: 10.11648/j.ijiis.20190801.13

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    Karim Moharm. A Framework for Adaptive Personalized E-learning Recommender Systems. Int J Intell Inf Syst. 2019;8(1):12-17. doi: 10.11648/j.ijiis.20190801.13

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  • @article{10.11648/j.ijiis.20190801.13,
      author = {Karim Moharm},
      title = {A Framework for Adaptive Personalized E-learning Recommender Systems},
      journal = {International Journal of Intelligent Information Systems},
      volume = {8},
      number = {1},
      pages = {12-17},
      doi = {10.11648/j.ijiis.20190801.13},
      url = {https://doi.org/10.11648/j.ijiis.20190801.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijiis.20190801.13},
      abstract = {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.},
     year = {2019}
    }
    

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