Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Towards Effective Usage-Based Learning Applications: Track and Learn from User Experience(s)
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Use of contextualized attention metadata for ranking and recommending learning objects
CAMA '06 Proceedings of the 1st international workshop on Contextualized attention metadata: collecting, managing and exploiting of rich usage information
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During their lifecycle, Learning Resources undergo a multitude of processes while being created, used, provided or re-used. However, in order to be reusable, a Learning Resource often has to be adapted to a new context of use. This in turn implies multiple Re-Authoring processes being performed on the Learning Resource. During all these processes different types of information emerge. When captured, this information can be helpful for a later on retrieval, use or re-use of the Learning Resources. In this work, the lifecycle of Learning Resources along with the information being generated herein is analyzed and a distributed architecture is proposed, that allows the capturing, processing, management and utilization of the named information in a generic way.