Abstract
This paper proposes the use of federated databases techniques in searching for educational resources by using a learning object paradigm that describes these resources based on metadata. Combining a complete agent-based architecture that implements the concept of federated search along with IR technologies may help organizing and sorting search results in a meaningful way for educational content. The paper presents also the ground for an approach for semantic-aware learning content retrieval based on abstraction layers between the repositories and the search clients.
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Gil, A.B., De la Prieta, F., Rodríguez, S. (2011). Automatic Learning Object Extraction and Classification in Heterogeneous Environments. In: Pérez, J.B., et al. Highlights in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol 89. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19917-2_14
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DOI: https://doi.org/10.1007/978-3-642-19917-2_14
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