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Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06)
Ownership Detection and Protection for Learning Objects
Kerkrade, The Netherlands
July 05-July 07
ISBN: 0-7695-2632-2
Hend Madhour, University of Lausanne, Switzerland
Mohamed Ali Sfaxi, University of Lausanne, Switzerland
Maia Wentland Forte, University of Lausanne, Switzerland
Solange Ghernaouti Helie, University of Lausanne, Switzerland
Reuse of learning objects often requires a threedimensional adaptation : content, context and display. The resulting new object must explicitly refer to the set it comes from when being inserted in a Learning Object Repository (LOR). Thus, we organize Learning objects in the LOR according to three levels (abstract, instantiation and presentation levels) that take into account those ownership sides. To guarantee a correct insertion of into the LOR, we designed a network matcher in order to integrate new learning objects into the Learning Object Repository and linking them to existing ones in the Learning Object Network. However, this organization allows only ownership definition but not its detection and protection. That?s why, we show limitations of existing ways to preserve ownership (especially the digital signature) and propose some possible solutions.
Citation:
Hend Madhour, Mohamed Ali Sfaxi, Maia Wentland Forte, Solange Ghernaouti Helie, "Ownership Detection and Protection for Learning Objects," icalt, pp.784-788, Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06), 2006
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