Open AccessOnline Learning Styles Identification Model, Based on the Analysis of User Interactions Within an E-Learning Platforms, Using Neural Networks and Fuzzy Logic
Author(s) -
Luca de Alfaro,
Claudia Rivera,
Jorge Luna-Urquizo,
Edgar E. Medina Castaneda,
Francisco Antônio Pereira Fialho
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.13.16328
Subject(s) - computer science , identification (biology) , artificial neural network , fuzzy logic , categorization , artificial intelligence , backpropagation , machine learning , preprocessor , botany , biology
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.