This paper proposes a pedagogical and technical approach to support the flow of learning activities outside of school and in class. One primary goal is to develop curricula that bring multimedia resources to outdoor settings to enrich the field experience, and to enable students to make connections between what they learn outside and the formal curriculum. A pilot user scenario and the supporting technology for a set of collaborative learning activities involving tasks of preparation, data gathering, data analyzing, visualization and modeling related to a diversity of content areas are described. The background for the mobile infrastructure, including a learning object repository, has been implemented and tested.

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We have begun to define and explore an agentbased distributed computational environment to support pervasive learning. In this article, we describe the system for configuring learning scenarios enhanced with an educational networking infrastructure including different kinds of devices. Students carry out indoor and outdoor activities with learning mediation tools either using classroom computers or mobiles devices, such as PDAs, to be used in labs or in field trips. All these activities are interwoven and supported in a seamless way by a learning repository, which is the backbone of the infrastructure. The configuration system is 'intelligent' enough to be able to deploy and initialize the tools for the student's activity taking into account the context of the user and the activity -localization, current working device, tools available-and personalize it according to the situated requirements. The set of specialist agents and the models needed are subsequently described, together with the benefits of the AI techniques for coping this problem.

This paper presents an educational networking architecture for integrating long-term learning processes. Learning products generated with a variety of tools can be shared and re-used across a workflow of computer supported activities. The architecture provides interoperability with heterogeneous learning tools and a configurable structure able to adapt to different learning scenarios. The architecture also includes an extensible metadata framework for characterizing and auto-documenting the learning products, as well as mechanisms for versioning and structuring them.