The evolution of computing technology and wireless networks has contributed to the miniaturization of mobile devices and their increase in power, providing services anywhere and anytime. In this scenario, applications have considered the user's contexts to make decisions (Context Awareness). Context-aware applications have enabled new opportunities in different areas, for example, education, games and entertainment, commerce, and competence management. In this article, we present MultCComp, a multi-temporal context-aware system for competences management. The main system contribution is to take advantage of the workers' present and past contexts to help them to develop their competences. We define as multi-temporal context awareness the joint use of workers' present and past contexts to assist them in the development of their competences. We developed a prototype and conducted two experiments with it in an evaluation environment. The first experiment aimed to demonstrate the system functionalities. It consisted of two evaluation scenarios that were followed by two users. The second experiment focused on evaluating the acceptance of the system. It comprised a scenario that was followed by 21 users, who filled out a questionnaire at the end of the test.

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In mobile computing environments, the tracking of users allows applications to adapt to the contexts visited by users (Context Awareness). In recent years, the use of context information and users' profiles has been considered an opportunity for context-aware content management. The improvement and the wide adoption of location systems are stimulating the tracking of users, allowing the use of Trails. A trail is the history of the contexts visited by a user during a period. This article proposes a model for trails management and its application in the content management. In this text, we consider that Trail-Aware is an evolution of the simple use of contexts and profiles. The text presents a prototype and its application in an educational environment for distribution of learning objects using trail-aware.

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