Personalized Ubiquitous Learning via an Adaptive Engine

Abstract

Nowadays, the world's population is increasingly waiting for permanent and constant access to information. Accessing the right information at any time and any place is becoming a necessity. A learning system is called ubiquitous if it is able to adapt itself to its context (user, platform, environment, device, etc.). In this sense, theories and methods of adaptations keep rolling in order to make learning processes more efficient and relevant. In this paper, we propose an approach for providing personalized course content in ubiquitous learning, considering learning styles and context-awareness. The proposed approach aims to support learners by presenting course materials generated by an adaptive engine based on adaptation rules.