Ownership Detection and Protection for Learning Objects

Reuse of learning objects often requires a three-dimensional 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 solution