A linked data-based collaborative annotation system for increasing learning achievements |
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| Authors: | Hafed Zarzour Mokhtar Sellami |
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| Institution: | 1.Department of Computer Science,University of Souk Ahras,Souk Ahras,Algeria;2.LABGED, Department Computer Science,University of Annaba,Annaba,Algeria |
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| Abstract: | With the emergence of the Web 2.0, collaborative annotation practices have become more mature in the field of learning. In this context, several recent studies have shown the powerful effects of the integration of annotation mechanism in learning process. However, most of these studies provide poor support for semantically structured resources, more precisely for sharing and linking educational contents using linked data approach. Adopting Semantic Web technologies, this paper proposed a new linked data-based collaborative annotation system called L2OD, which allows students to enrich their annotations with relevant resources retrieved from Linked Open Data clouds. L2OD supports two modes of annotation, a private annotation mode for the user’s individual annotations, and a shared mode for all users’ annotations. The experimentation shows that the learners who have used L2OD have significantly increased their learning achievements referring to the difference between L2OD’s post-test and the control group’s post-test. It also shows significant positive correlations between learning achievements and quantity of private and shared annotations in the experimental group, respectively. |
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