Semantic-based automated composition of distributed learning objects for personalized e-learning

Recent advances in e-learning techonologies and web services make realistic the idea that courseware for personalized e-learning can be built by dynamic composition of distributed learning objects, available as web-services. To be assembled in an automated way, learning objects metadata have to be exploited, associating unambiguous and semantically rich descriptions, to be used for such an automated composition. To this aim, we present a framework and algorithms for semantic-based learning objects composition, fully compliant with Semantic Web technologies. In particular our metadata refer to ontologies built on a subset of OWL-DL, and we show how novel inference services in Description Logics can be used to compose dynamically, in an approximated -but computationally tractable - way learning resources, given a requested courseware description. © Springer-Verlag Berlin Heidelberg 2005.