The Ensemble Project: Using Fedora to Support the Development of the Semantic Web for Education
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The Semantic Web and associated Semantic Technologies (standards, services, tools, triplestores, reasoners and visualisers) have, it has been argued, the potential to support and enhance teaching and learning; but, as yet, this potential has not been practically demonstrated in ways that are accessible to teachers and learners. 'Ensemble: Semantic Technologies to Support the Teaching and Learning of Case Based Learning' (http://www.ensemble.ac.uk) is a major UK-based project funded under the ESRC/EPSRC Technology Enhanced Learning Programme. It is currently exploring the potential of Semantic Technologies to support and enhance teaching and learning in fields where knowledge is complex, changing or contested, and where, as a result, case-based learning is the pedagogy of choice. An interdisciplinary research team from five UK Universities is working with participants in disciplines as diverse as Law, Engineering, Journalism and Biological Sciences. Fedora has been selected as the most technological platform to support the work of the project, which is currently undertaking parallel strands of work: to identify existing practices and patterns of discourse in teaching and learning environments, and to assess the challenges of providing appropriate data and other resources to support these practices. The inbuilt Semantic functions of Fedora, its support for complex digital objects, and the integration with the Mulgara triplestore make it the most appropriate choice for projects of this kind. The implementation of Fedora is distinctive in several respects. Firstly, the repository is used not only to expose metadata about digital objects for subsequent download, but also to serve live RDF/XML as a datastream. A set of custom scripts then populate an external Mulgara triplestore with data, metadata and rules allowing users to reason across all of these. Results are then presented using tools from the SIMILE Semantic Web toolkit developed at MIT. These approaches make it possible for teachers and learners to integrate real data sets into teaching and learning environments, and to use these in problem formulation, hypothesis testing and analysis. It represents a 'step change' in applications of Semantic technologies to education, moving beyond federated search and data aggregation, and providing a basis for analysis and knowledge construction. This paper will describe how the project has developed its Fedora and Mulgara implementations in the light of emerging understanding of teaching and learning demands, and will be illustrated by examples of prototype learning environments that have been developed.