Matches in ScholarlyData for { ?s ?p ?o. }
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent davide-taibi.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent giovanni-panasci.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent biagio-lenzitti.
- item-3 previous item-2.
- 26 creator anastasia-dimou.
- 26 creator charalampos-bratsas.
- 26 creator ioannis-antoniou.
- 26 creator lazaros-ioannidis.
- 26 creator panagiotis-bamidis.
- 26 type InProceedings.
- 26 label "Semantic CMS and Wikis as platforms for Linked Learning".
- 26 sameAs 26.
- 26 abstract "Although interoperability has always been a priority in e-learning, conventional Learning Management Systems are geared mostly towards Standards for Learning Objects exchange and integration among them. They hardly foresee the contingency for integration with other web applications and data. This prevents them, nowadays, from being flexible to adapt to the Linked Data standards emergence and the advent of Semantic Web in general, unless they radically change orientation. In contrast, Wikis, followed by Content Management Systems, proved to be more versatile in complying with the Semantic Web and Linked Data standards. These advancements, together with their modular architecture, turn Wikis and CMSs into a decent choice for modern e-learning solutions. MediaWiki and Drupal were customized and deployed in the Aristotle University of Thessaloniki to assess their potential in exposing the University’s learning resources on the Web of Linked Data in accordance to the Linked Universities Initiative. On the occasion of these two deployments, a thorough comparison of their platforms’ potentials to function as Learning Management Systems took place and is presented on this paper.".
- 26 hasAuthorList authorList.
- 26 isPartOf proceedings.
- 26 keyword "Linked Data".
- 26 keyword "Linked Learning.".
- 26 keyword "Semantic CMS".
- 26 keyword "Semantic Wiki".
- 26 title "Semantic CMS and Wikis as platforms for Linked Learning".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasItem item-5.
- authorList hasLastItem item-5.
- item-1 type ListItem.
- item-1 hasContent charalampos-bratsas.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent anastasia-dimou.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent lazaros-ioannidis.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent panagiotis-bamidis.
- item-4 next item-5.
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent ioannis-antoniou.
- item-5 previous item-4.
- 28 creator muriel-foulonneau.
- 28 creator valentin-groues.
- 28 type InProceedings.
- 28 label "Common vs. Expert knowledge: making the Semantic Web an educational model".
- 28 sameAs 28.
- 28 abstract "Model based learning uses models to store knowledge in order to generate learning resources and adapt learning paths to learners and their context. Many domain models are published on the Web through linked data, thus providing a collective knowledge base that can be reused in the educational domain. However, for these models to be usable in an educational context, they must allow predicting the learning context in which they can be used. A typical indicator of the usability of learning object is their difficulty. Predicting the difficulty of an assessment item depends both on the construct, i.e., what is assessed, and on the form of the item. In this paper, we present several experiments that can support the prediction of the difficulty of an assessment item generated from a linked data source and the difficulty of the underlying item construct. We analyze the results of a test carried out with choice items, together with a Web mining approach, in order to provide indicators that the factual knowledge is common knowledge or expert knowledge in a particular population. Our objective is to annotate the semantic models and increase their reusability in an educational context.".
- 28 hasAuthorList authorList.
- 28 isPartOf proceedings.
- 28 keyword "Linked Data".
- 28 keyword "Semantic Web".
- 28 keyword "assessment item generation".
- 28 keyword "educational model".
- 28 keyword "knowledge level".
- 28 keyword "paradata".
- 28 title "Common vs. Expert knowledge: making the Semantic Web an educational model".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent muriel-foulonneau.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent valentin-groues.
- item-2 previous item-1.
- 29 creator aleksandar-giljanovic.
- 29 creator dragan-gasevic.
- 29 creator jelena-jovanovic.
- 29 creator liaqat-ali.
- 29 creator marek-hatala.
- 29 creator melody-siadaty.
- 29 creator nikola-milikic.
- 29 creator zoran-jeremic.
- 29 type InProceedings.
- 29 label "Semantic Web and Linked Learning to Support Workplace Learning".
- 29 sameAs 29.
- 29 abstract "In the last few years, the Social Web has offered new affordances for how learning is conceptualized and supported. Supporting workplace learning, however, faces specific challenges, some in particular due to its informal, contextual and social nature. The in-formal nature of workplace learning requires knowledge workers to be supported in their self-regulatory learning processes, whilst the social side draws attention to the role of collective in those processes. To address these challenges, in this paper we present Learn-B, a workplace learning environment. We also present how we developed and applied a common ontological foundation for the integration of our proposed learning services and existing tools in this environment.".
- 29 hasAuthorList authorList.
- 29 isPartOf proceedings.
- 29 keyword "linked data".
- 29 keyword "organizational learning".
- 29 keyword "self-regulated learning".
- 29 keyword "semantic web technologies".
- 29 keyword "workplace learning".
- 29 title "Semantic Web and Linked Learning to Support Workplace Learning".
- authorList type List.