Online Learning Styles Identification Model, Based on the  Analysis of User Interactions Within an E-Learning Platforms, Using Neural Networks and Fuzzy Logic

Alfaro, Luca de; Rivera, Claudia; Luna-Urquizo, Jorge; Castaneda, Edgar E. Medina; Fialho, Francisco Antônio Pereira

doi:10.14419/ijet.v7i3.13.16328

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…6. Variation of user identifications through several learning sessions [27] This phenomenon is relatively normal, given the proximity among some learning styles and the mixed preferences of some students, which also can be appreciated in Table I and Figure 6, where, for example, the set of students with mixed preferences is the second most representative, in some cases the task of identifying just one learning style being very complex.…”

Section: Resultsmentioning

confidence: 87%

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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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Section: Resultsmentioning

confidence: 87%

“…5. Fuzzy sets for user preference for a certain resource category [27] application of the concept of 'fuzzy logic' permits the modeling of situations which do not have precise limits, which makes for a much more realistic model, especially when having to do with cognitive and subjective aspects.…”

Section: Proposal and Implementation Of The Neuro-fuzzy Systemmentioning

confidence: 99%

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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show abstract

“…Fuzzy system This module is based on the previous work of the authors [25], for the collection of test data, we applied the learning styles questionnaire proposed by Honey [7], to a group of 70 higher education students of the National University of SanAgustín during the first semester of the year 2017, so that the Learning Style of each student was known in advance according to a traditional method.…”

Section: Online Identification Of Learning Style Using a Neuralmentioning

confidence: 99%

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

Alfaro¹,

Rivera²,

Luna-Urquizo

et al. 2018

Proceedings of the 16th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Innovation in Educat

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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.

show abstract

Section: Online Identification Of Learning Style Using a Neuralmentioning

confidence: 99%

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

Alfaro¹,

Rivera²,

Luna-Urquizo³

et al. 2018

EasyChair Preprints

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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.

show abstract

Online Learning Styles Identification Model, Based on the Analysis of User Interactions Within an E-Learning Platforms, Using Neural Networks and Fuzzy Logic

Cited by 4 publications

References 11 publications

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

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

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

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