A model for profile management applied to ubiquitous learning environments

Ubiquitous systems have the challenge of implicitly collect relevant information about entities, and use this information to understand and predict their behavior. This allows the applications to adapt themselves to the entities, thus avoiding to overflow them with inquires and information. The analysis of trails, the context-aware history of actions, can further improve the relevance of information. This paper proposes a model that allows applications to register entities’ actions in trails and infer profile information from these trails, using semantic interoperability and thus allowing different applications to share information and infer a unified profile. An application was developed and integrated with two different softwares in a scenario of ubiquitous learning, where the student profiles were dynamically updated, allowing them to better adapt to the environment. The contributions of this model are the use of trails for extracting profiles and the capability of managing dynamic inference rules for profile generation.