Using Learning Styles and Neural Networks as an Approach to eLearning Content and Layout Adaptation

Abstract. The eLearning’s trend is changing; learning content has become the key issue of current eLearning. The eLearning in Portugal as in many other countries is not yet so widely used as an alternative to other forms of training: as is the case of traditional classroom. This is because learners don’t identify their own learning style in the way the presentation of education content are done in the majority of eLearning material produced today, or not feel enough customization in the content to their own needs. This paper describes the design, development and implementation of the model of an adaptive course player that uses Kolb learning styles[1] and neural networks to model learners and dynamically generates navigation paths and layout adaptation. The system implements adaptation of individual recommendations and content adaptation based on learning styles, previous learner knowledge, learner’s progress and persistence of their own preferences. This is a ongoing work, and we are using our own experience producing eLearning content and an actual eLearning project to evolve the way difficult domain content can be presented to different individuals or stereotyped groups (similar conceptual understanding) with a disparity of objectives, different kind of professional roles, dissimilar previous knowledge and different context