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.
|Number of pages||9|
|Journal||International Journal of Advanced Computer Science and Applications|
|State||Published - 2018|
Bibliographical noteFunding Information:
The authors would like to thanks to Saint Augustin National University (UNSA) for supporting this research.
© 2018 International Journal of Advanced Computer Science and Applications.
- Backpropagation neural network
- Fuzzy logic
- Learning style identification
- Neuro fuzzy systems