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


Fuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component
Published in:	Innovation in Education and Inclusion : Proceedings of the 16th LACCEI International Multi-Conference for Engineering, Education and Technology
Date of Conference:	July 18-20, 2018
Location of Conference:	Lima, Perú
Authors:	Luis Alfaro (Universidad Nacional de San Agustin, PE) Claudia Rivera (Universidad Nacional de San Agustin, PE) Jorge Luna-Urquizo (Universidad Nacional de San Agustin, PE) Elisa Castañeda (Universidad Nacional de San Agustin, PE) Francisco Fialho (Universidad Nacional de San Agustin, PE)
Full Paper:	#259

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 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.