A Mobile and Adaptive Language Learning Environment based on Linked Data

The possibilities within e-learning environments increased dramatically the last couple of years. They are more and more deployed on the Web, allow various types of tasks and fine-grained feedback, and they can make use of audiovisual material. On the other hand, we are confronted with an increasing heterogeneity in terms of end-user devices (smartphones, tablet PCs, etc.) that are able to render advanced Web-based applications and consume multimedia content. Therefore, the major contribution of this paper is an adaptive, Web-based e-learning environment that is able to provide rich, personalized e-learning experiences to a wide range of devices. We discuss the global architecture and data models, as well as how the integration with media delivery can be realized. Further, we give a detailed description of a reasoner, which is responsible for the adaptive selection of learning items, based on the usage environment and the user profile.