An infrastructure for context-awareness based on first order logic
Personal and Ubiquitous Computing
An ontology for context-aware pervasive computing environments
The Knowledge Engineering Review
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PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
A Multi-Model Approach for Supporting the Personalization of Ubiquitous Learning Applications
WMTE '05 Proceedings of the IEEE International Workshop on Wireless and Mobile Technologies in Education
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PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
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SEUS-WCCIA '06 Proceedings of the The Fourth IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems, and the Second International Workshop on Collaborative Computing, Integration, and Assurance (SEUS-WCCIA'06)
Context Model and Context Acquisition for Ubiquitous Content Access in ULearning Environments
SUTC '06 Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing - Vol 2 - Workshops - Volume 02
Situation Awareness: Dealing with Vague Context
PERSER '06 Proceedings of the 2006 ACS/IEEE International Conference on Pervasive Services
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
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International Journal of Technology Enhanced Learning
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Proceedings of the 28th ACM International Conference on Design of Communication
Design and Evaluation of a Project-Based Learning Ubiquitous Platform for Universal Client: PBL2U
International Journal of Mobile and Blended Learning
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Pervasive learning systems must define new mechanism to deliver the right resource, at the right time, at the right place to the right learner. This means that rich context information has to be considered: time, place, user knowledge, user activity, user environment and device capacity. As context is based on numerous information which may change frequently (coming from a collection of captors), a more aggregate view is defined to work on more abstract objects: the situations. Context information and situation information have to be widespread into all the models of learning systems: context preferences have to be handled in the learner model, well-adapted situation and situation scenarios have to be memorized in learning resource model. The adaptation process is enriched too.