Utilization of a Neuro Fuzzy Model for the Online Detection of Learning Styles in Adaptive e-Learning Systems
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 12
Abstract
After conducting a historical review and establi-shing 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.
Authors and Affiliations
Luis Alfaro, Claudia Rivera, Jorge Luna-Urquizo, Elisa Castaneda, Francisco Fialho
Keywords
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- EP ID EP429077
- DOI 10.14569/IJACSA.2018.091202
- Views 92
- Downloads 0
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