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

  • Authors

    • L Alfaro
    • C Rivera
    • J Luna-Urquizo
    • E Castañeda
    • F Fialho
    2018-07-27
    https://doi.org/10.14419/ijet.v7i3.13.16328
  • Backpropagation Neural Network, e-Learning, Fuzzy Logic, Learning Styles.

  • Abstract

    Individual Learning Style identification is an essential aspect in the development of intelligent or adaptive e-Learning platforms. Traditional methods are based on the application of questionnaires or psychological tests, which may not be the most appropriate in all cases. The proposed model is based on the analysis of user behavior through the study of their interactions within an e-Learning platform, using a multilayer Backpropagation Neural Network and Fuzzy Logic concepts, for the preprocessing of the inputs and the categorization of the outputs.

                                                                                

     

     

  • References

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  • How to Cite

    Alfaro, L., Rivera, C., Luna-Urquizo, J., Castañeda, E., & Fialho, F. (2018). Online Learning Styles Identification Model, Based on the Analysis of User Interactions Within an E-Learning Platforms, Using Neural Networks and Fuzzy Logic. International Journal of Engineering & Technology, 7(3.13), 76-78. https://doi.org/10.14419/ijet.v7i3.13.16328

    Received date: 2018-07-26

    Accepted date: 2018-07-26

    Published date: 2018-07-27