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A semantic description of learning objects based on an ontology

Author(s): Amel BOUZEGHOUB | Bruno DEFUDE | John-Freddy DUITAMA | Claire LECOCQ

Journal: STICEF
ISSN 1764-7223

Volume: 12;
Issue: 2005;
Start page: 205;
Date: 2005;
Original page

Keywords: Educational metadata | learning object | indexation | assembling | reusing

The number of learning objects available on the Internet has significantly grown these last years and the problem of indexing and searching these learning objects is becoming crucial. Standards and norms of educative metadata such as LOM and SCORM have been proposed to handle this problem but in our opinion these proposals are not a satisfactory solution. In this paper, we propose to extend these standards with a semantic description of learning objects based on an ontology. A learning object is described by prerequisites, a content and an acquisition function. This allows defining powerful search tools and improves reusing. For reusing, we propose to define new learning objects by assembling existing objects. Assembling is specified by a composition graph composed by learning objects and composition operators (sequence, parallel, and alternative). In order to improve flexibility, we have introduced intentional objects. An intentional object is defined by a composition graph where (at least) one object has been replaced by an intentional query on the learning object repository. This model is used during the adaptive process of a learning object for a specific learner. We define a notion of quality on learning objects which mainly reflects their ability to reuse. This quality may be evaluated a posteriori using metrics on objects or controlled a priori using a type system. Our model has been implemented with Sesame, a RDF database which support SeRQL a powerful query language for RDF and RDFS.
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