Fuzzy neural system model for online learning styles identification, as an adaptive hybrid e-learning system architecture component

L. Alfaro, C. Rivera, J. Luna-Urquizo, E. Castañeda, F. Fialho

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In the present work, we present a Fuzzy Neural System Model for online identification of Learning Styles which gives support for contents personalization. The model was developed to serve as a component for an Adaptive Hybrid E-Learning System Architecture, which focus on a high degree of customization and content adaptation. We proposal a Hybrid System model, in which techniques of Neural Networks, Fuzzy Logic and Case Based Reasoning are incorporated into the multiagent system. Finally, the authors present the architecture of the Fuzzy Neural System model, the results of the analysis of the model validation tests establishing conclusions and recommendations.

Original languageEnglish
Title of host publication16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtitle of host publicationInnovation, Education, and Inclusion
PublisherLatin American and Caribbean Consortium of Engineering Institutions
ISBN (Electronic)9780999344316
DOIs
StatePublished - 2018
Event16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology - Lima, Peru
Duration: 18 Jul 201820 Jul 2018

Publication series

NameProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volume2018-July
ISSN (Electronic)2414-6390

Conference

Conference16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Country/TerritoryPeru
CityLima
Period18/07/1820/07/18

Bibliographical note

Publisher Copyright:
© 2018 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Keywords

  • Adaptive Systems
  • Artificial Neural Networks
  • E-Learning
  • Fuzzy Logic
  • Fuzzy Neural Systems
  • Hybrid architecture
  • Multiagent Systems

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