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

Alfaro, Luis; Rivera, Claudia; Luna-Urquizo, Jorge; Castaneda, Elisa; Fialho, Francisco Antônio Pereira

doi:10.18687/laccei2018.1.1.259

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…The architecture of the proposed mulit-agent model, as with the description of its components, can be found in Alfaro et al [11], which is shown in Figure 1. Fig.…”

Section: B Proposal For E-learning Adaptive System Architecturementioning

confidence: 99%

“…1. Architecture of the multi-agent system [11] The implementation of the proposed intelligent agents was realized utilizing the JADE platform, which is an agent platform distributed with a container for each host, in which the agents are executed and which possesses storage for diverse languages and ontologies, complying with FIPA (Foundation for Intelligent Physical Agents) specifications, for which developed agents can easily be integrated in other languages and platforms, including owners.…”

Section: B Proposal For E-learning Adaptive System Architecturementioning

confidence: 99%

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Utilization of a Neuro Fuzzy Model for the Online Detection of Learning Styles in Adaptive e-Learning Systems

Alfaro¹,

Rivera²,

Luna-Urquizo

et al. 2018

ijacsa

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After conducting a historical review and establishing the state of the art of the various approaches regarding the design and implementation of adaptive e-learning systems-taking into consideration the characteristics of the user, in particular their learning styles and preferences in order to focus on the possibilities for personalizing the ways of utilizing learning materials and objects in a manner distinct from what e-learning systems have traditionally been, which is to say designed for the generic user, irrespective of individual knowledge and learning styles-the authors propose a system model for the classification of user interactions within an adaptive e-learning platform, and its analysis through a mechanism based on backpropagation neural networks and fuzzy logic, which allow for automatic, online identification of the learning styles of the users in a manner which is transparent for them and which can also be of great utility as a component of the architecture of adaptive e-learning systems and knowledge-management systems. Finally, conclusions and recommendations for future work are established.

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“…The architecture of the proposed mulit-agent model, as with the description of its components, can be found in Alfaro et al [11], which is shown in Figure 1. Fig.…”

Section: B Proposal For E-learning Adaptive System Architecturementioning

confidence: 99%

Section: B Proposal For E-learning Adaptive System Architecturementioning

confidence: 99%