Personalisation Services for Self e-Learning Networks
| Autor: | Miltos Stratakis, Peter T. Wood, Philippe Rigaux, George Papamarkos, Aimilia Magkanaraki, Nicolas Spyratos, Alexandra Poulovassilis, Kevin Keenoy, Vassilis Christophides |
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| Přispěvatelé: | School of Computer Science and Information Systems, Queen Mary University of London (QMUL), Institute of Computer Science (ICS-FORTH), Foundation for Research and Technology - Hellas (FORTH), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2004 |
| Předmět: |
Multimedia
Event (computing) Computer science InformationSystems_INFORMATIONSYSTEMSAPPLICATIONS E-learning (theory) 05 social sciences peer-to-peer learning networks 050301 education resource description frame (RDF) 02 engineering and technology Learning object metadata computer.software_genre Personalization World Wide Web Metadata personalisation 020204 information systems 0202 electrical engineering electronic engineering information engineering [INFO.EIAH]Computer Science [cs]/Technology for Human Learning Web service 0503 education computer |
| Zdroj: | Proceedings of 2nd IST Workshop on Metadata Management in Grid and P2P Systems: Models, Services and Architectures (MMGPS'04), London, December 17, 2004. 2nd IST Workshop on Metadata Management in Grid and P2P Systems: Models, Services and Architectures (MMGPS'04) 2nd IST Workshop on Metadata Management in Grid and P2P Systems: Models, Services and Architectures (MMGPS'04), 2004, London, United Kingdom. 14 p Lecture Notes in Computer Science ISBN: 9783540225119 ICWE |
| Popis: | This paper describes the personalisation services designed for self e-learning networks in the SeLeNe project. A self e-learning network consists of web-based learning objects that have been made available to the network by its users, along with metadata descriptions of these learning objects and of the network's users. The proposed personalisation facilities include: querying learning object descriptions to return results tailored towards users' individual goals and preferences; the ability to define views over the learning object metadata; facilities for defining new composite learning objects; and facilities for subscribing to personalised event and change notification services. We show the feasibility of automatically deriving descriptions for composite learning objects and of realising the personalisation facilities using a service-based architecture, employing a combination of existing and new Semantic Web technologies including RDF/S, RQL, RVL, and ECA rules. |
| Databáze: | OpenAIRE |
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