Nowadays, e-learning offers advantages over traditional learning in terms of independence. Moreover, adaptive e-learning systems take into account learner's profile, such as learning style and level of knowledge, in order to provide the most appropriate learning object. However, the essential challenge is finding and identifying the learning objects from a big corpus while ensuring their independence in different contexts. To overcome these problems of interoperability and accessibility of learning objects, the authors proposed to define a learning semantic Web service for each learning object. This service is an extension of OWLS that encompasses the description of the learning intention and the use of context that characterize a learning object. In this paper, the authors propose a new discovery mechanism based on learning intention and context use guided by the learner's intention and profile in order to offer a personalized learning path. Experimental results prove the efficiency of the proposed approach and approve its notable contribution.
International audienceIn the present work, we presented a new approach to provide learners with learning paths adapted to their requests. These courses were generated by the composition of the learning semantic Web services. Our approach defined a learning Web service (WS) for each learning object (LO) to overcome the problems of interoperability and accessibility of learning objects. Each WS was represented, in the directory, by a learning semantic Web service (OWLS-LO) to describe a semantic understanding of the object being represented. This semantic approach was described by ontologies
Nowadays, e-learning offers advantages over traditional learning in terms of independence. Moreover, adaptive e-learning systems take into account learner's profile, such as learning style and level of knowledge, in order to provide the most appropriate learning object. However, the essential challenge is finding and identifying the learning objects from a big corpus while ensuring their independence in different contexts. To overcome these problems of interoperability and accessibility of learning objects, the authors proposed to define a learning semantic Web service for each learning object. This service is an extension of OWLS that encompasses the description of the learning intention and the use of context that characterize a learning object. In this paper, the authors propose a new discovery mechanism based on learning intention and context use guided by the learner's intention and profile in order to offer a personalized learning path. Experimental results prove the efficiency of the proposed approach and approve its notable contribution.
E-learning systems use web service technology to develop distributed applications. Therefore, with the tremendous growth in the number of web services, finding the proper services while ensuring the independence and reusability of the learning objects in a different context has become an important issue and has attracted much interest. This article first proposes an extension of the Ontology Web Language for Services Learning Object (OWLS-LO) model to describe a multi-intentional learning object. This description ensures accessibility to learning objects. This research then presents a service discovery mechanism that uses the new semantic model for service matching. Experimental results show that the proposed semantic discovery mechanism using multi-intention model performs better than discovery mechanism based on single intention.
in this work, we propose an approach of thematisation of audiovisual (AV) documents for a research according to topics evoked in each document. The first step of our approach is to define the descriptive metadata allowing a bibliographical description of the whole documents. The second step is divided into three stages: the first one is a temporal segmentation, the second one is space segmentation and the last is a semantic thematized description. Topics are modeled by classes of annotations such as (person, event, site and activity, etc.) and their instances (such as an event political, sporting, and cultural, etc.).In order to illustrate our work of semantic annotation we used platform ADVENE. This one will create semantic annotations by video and thereafter generating hypervideos for an active reading. This platform thus makes practices of thematized reading it possible. After this experiment, we will see to which extent and by which means our solution of thematisation, will give a better search of heterogeneous audiovisual documents.
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