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

EasyChair Preprint 261

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.

Keyphrases: Artificial Neural Networks, Multiagent Systems, adaptive systems, e-learning, fuzzy neural systems

@booklet{EasyChair:261,
  author    = {Luis Alfaro and Claudia Rivera and Jorge Luna-Urquizo and Elisa Castañeda and Francisco Fialho},
  title     = {Fuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component},
  doi       = {10.29007/z2jh},
  howpublished = {EasyChair Preprint 261},
  year      = {EasyChair, 2018}}