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DOI: 10.14569/IJACSA.2019.0100752
PDF

Proposal Models for Personalization of e-Learning based on Flow Theory and Artificial Intelligence

Author 1: Anibal Flores
Author 2: Luis Alfaro
Author 3: José Herrera
Author 4: Edward Hinojosa

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 7, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: This paper presents the comparison of the results of two models for the personalization of learning resources sequences in a Massive Online Open Course (MOOC). The compared models are very similar and differ just in the way how they recommend the learning resource sequences to each participant of the MOOC. In the first model, Case Based Reasoning (CBR) and Euclidean distance is used to recommend learning resource sequences that were successful in the past, while in the second model, the Q-Learning algorithm of Reinforcement Learning is used to recommend optimal learning resource sequences. The design of the learning resources is based on the flow theory considering dimensions as knowledge level of the student versus complexity level of the learning resource with the aim of avoiding the problems of anxiety or boredom during the learning process of the MOOC.

Keywords: Massive Online Open Course; MOOC; e-learning; flow-theory; learning resource sequence; case based reasoning; reinforcement learning; q-learning

Anibal Flores, Luis Alfaro, José Herrera and Edward Hinojosa, “Proposal Models for Personalization of e-Learning based on Flow Theory and Artificial Intelligence” International Journal of Advanced Computer Science and Applications(IJACSA), 10(7), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100752

@article{Flores2019,
title = {Proposal Models for Personalization of e-Learning based on Flow Theory and Artificial Intelligence},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100752},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100752},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {7},
author = {Anibal Flores and Luis Alfaro and José Herrera and Edward Hinojosa}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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