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.

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International audienceOne issue for context-aware applications is to identify without delay situations requiring reactions. The identification of these situations is computed from both dynamic context information and domain specific knowledge. This identification is the output of a process involving context interpretation, aggregation and deduction. In smart environments, these treatments have to be efficient since they may be partly performed on constrained mobile devices. Two main approaches exist in the literature: process-oriented and ontology-based context management. In this paper, we claim that they are complementary and we propose an architecture which integrates the two approaches. We show in a scenario how context-aware applications can benefit from this architecture both to scale to numerous mobile users and to identify complex situation

The chapter is organized as follows: the authors introduce some issues of technology-enhanced learning systems and define mobile, pervasive and ubiquitous learning and some closely related features: context, adaptation, situated learning, working and learning activities. Secondly, work-based learning features are described. Thirdly, situation-based and activity-based learning strategies are presented. Finally, the P-LearNet project is used to illustrate the proposal, and the conclusion summarizes the chapter and shows how and at which level this framework can be reused.