Fuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component

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dc.contributor.authorAlfaro, Luis
dc.contributor.authorRivera, Claudia
dc.contributor.authorLuna-Urquizo, Jorge
dc.contributor.authorCastaƱeda, Elisa
dc.contributor.authorFialho, Francisco
dc.date.accessioned2018-12-17T03:07:59Z
dc.date.accessioned2022-04-04T16:25:04Z
dc.date.available2018-12-17T03:07:59Z
dc.date.available2022-04-04T16:25:04Z
dc.date.issued2018-09
dc.description.abstractIn 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 ELearning 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.en_US
dc.description.countryPeruen
dc.description.institutionUniversidad Nacional de San Agustinen
dc.description.trackTechnology for Teaching and Learning, E-learning and Distance Educationen
dc.identifier.isbn978-0-9993443-1-6
dc.identifier.issn2414-6390
dc.identifier.otherhttp://laccei.org/LACCEI2018-Lima/meta/FP259.html
dc.identifier.urihttp://dx.doi.org/10.18687/LACCEI2018.1.1.259
dc.identifier.urihttp://axces.info/handle/10.18687/2018102_259
dc.journal.referatopeerReview
dc.language.isoEnglishen_US
dc.publisherLACCEI Inc.en_US
dc.rightsLACCEI License
dc.rights.urihttps://laccei.org/blog/copyright-laccei-papers/
dc.subjecte-Learningen_US
dc.subjectMultiagent Systemsen_US
dc.subjectAdaptive Systemsen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectFuzzy Neural Systemsen_US
dc.titleFuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component
dc.typeArticleen_US

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