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

doi:10.18687/laccei2018.1.1.259

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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtítulo de la publicación alojada	Innovation, Education, and Inclusion
Editorial	Latin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)	9780999344316
DOI	https://doi.org/10.18687/laccei2018.1.1.259
Estado	Publicada - 2018
Evento	16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology - Lima, Perú Duración: 18 jul. 2018 → 20 jul. 2018

Serie de la publicación

Nombre	Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen	2018-July
ISSN (versión digital)	2414-6390

Conferencia

Conferencia	16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
País/Territorio	Perú
Ciudad	Lima
Período	18/07/18 → 20/07/18

Nota bibliográfica

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

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10.18687/laccei2018.1.1.259

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Alfaro, L., Rivera, C., Luna-Urquizo, J., Castañeda, E., & Fialho, F. (2018). Fuzzy neural system model for online learning styles identification, as an adaptive hybrid e-learning system architecture component. En 16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Innovation, Education, and Inclusion (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2018-July). Latin American and Caribbean Consortium of Engineering Institutions. https://doi.org/10.18687/laccei2018.1.1.259

Alfaro, L. ; Rivera, C. ; Luna-Urquizo, J. et al. / Fuzzy neural system model for online learning styles identification, as an adaptive hybrid e-learning system architecture component. 16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Innovation, Education, and Inclusion. Latin American and Caribbean Consortium of Engineering Institutions, 2018. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

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

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.",

keywords = "Adaptive Systems, Artificial Neural Networks, E-Learning, Fuzzy Logic, Fuzzy Neural Systems, Hybrid architecture, Multiagent Systems",

author = "L. Alfaro and C. Rivera and J. Luna-Urquizo and E. Casta{\~n}eda and F. Fialho",

note = "Publisher Copyright: {\textcopyright} 2018 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.; 16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology ; Conference date: 18-07-2018 Through 20-07-2018",

year = "2018",

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language = "Ingl{\'e}s",

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Alfaro, L , Rivera, C, Luna-Urquizo, J, Castañeda, E & Fialho, F 2018, Fuzzy neural system model for online learning styles identification, as an adaptive hybrid e-learning system architecture component. En 16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Innovation, Education, and Inclusion. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, vol. 2018-July, Latin American and Caribbean Consortium of Engineering Institutions, 16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, Lima, Perú, 18/07/18. https://doi.org/10.18687/laccei2018.1.1.259

Fuzzy neural system model for online learning styles identification, as an adaptive hybrid e-learning system architecture component. / Alfaro, L.; Rivera, C.; Luna-Urquizo, J. et al.
16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Innovation, Education, and Inclusion. Latin American and Caribbean Consortium of Engineering Institutions, 2018. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2018-July).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Fuzzy neural system model for online learning styles identification, as an adaptive hybrid e-learning system architecture component

AU - Alfaro, L.

AU - Rivera, C.

AU - Luna-Urquizo, J.

AU - Castañeda, E.

AU - Fialho, F.

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AB - 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.

KW - Adaptive Systems

KW - Artificial Neural Networks

KW - E-Learning

KW - Fuzzy Logic

KW - Fuzzy Neural Systems

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KW - Multiagent Systems

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BT - 16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology

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Alfaro L , Rivera C, Luna-Urquizo J, Castañeda E, Fialho F. Fuzzy neural system model for online learning styles identification, as an adaptive hybrid e-learning system architecture component. En 16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Innovation, Education, and Inclusion. Latin American and Caribbean Consortium of Engineering Institutions. 2018. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology). doi: 10.18687/laccei2018.1.1.259

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

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