Learning Style Recognition using Artificial Neural Network for Adaptive User Interface in E-learning

Abstract

Traditionally e-learning systems are emphasized on the online content generation and most of them fail in considering the requirements and learning styles of end user, while representing it. Therefore, appears the need for adaptation to the user’s learning behavior. Adaptive e-learning refers to an educational system that understands the learning content and the user interface according to pedagogical aspects. End users have unique ways of learning which may directly and indirectly affect on the learning process and its outcome. In order to implement effective and efficient e-learning, the system should be capable not only in adapting the content of course to the individual characteristics of students but also concentrate on the adaptive user interface according to students’ requirements. In this paper, at initial stage we are presenting an approach to recognize the learning styles of individual student according to the actions or navigations that he or she has performed on an e-learning application. This recognition technique is based on Machine Learning algorithm called Artificial Neural Networks and Web Usage Mining.