Learning Personalisation Approach Based on Resource Description Framework

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Foilsithe in:European Conference on e-Learning (Oct 2016), p. 323-330
Príomhchruthaitheoir: Jevsikova, Tatjana
Rannpháirtithe: Berniukevièius, Andrius, Kurilovas, Eugenijus
Foilsithe / Cruthaithe:
Academic Conferences International Limited
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Rochtain ar líne:Citation/Abstract
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045 2 |b d20161001  |b d20161031 
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100 1 |a Jevsikova, Tatjana 
245 1 |a Learning Personalisation Approach Based on Resource Description Framework 
260 |b Academic Conferences International Limited  |c Oct 2016 
513 |a Feature 
520 3 |a The paper is aimed to analyse the problem of learning personalisation applying Resource Description Framework (RDF) standard model. Research results are two-fold: first, the results of systematic literature review on RDF application in learning are presented, and, second, RDF-based learning personalisation approach is proposed. First of all, systematic literature review was conducted in Thomson Reuters Web of Science database and using Semantic Scholar search tool. The review has shown that RDF data model is based upon the idea of making statements about web resources in the form of subject-predicate-object expressions. These expressions are known as triples in RDF terminology. The subject denotes the resource, and the predicate denotes traits or aspects of the resource and expresses a relationship between the subject and the object. The review revealed that linked data and triples-based RDF standard model could be successfully used in education. On the other hand, although linked data approach and RDF standard model are already well-known in scientific literature, only few authors have analysed its application to personalise learning process, but many authors agree that linked data and RDF-based learning personalisation trends should be further analysed. Original RDF-based learning personalisation approach is also presented in the paper. According to this approach, RDF-based personalisation of learning should be based on applying students' learning styles and intelligent technologies. The main advantages of this approach are analyses of interconnections between students' learning styles and suitable learning components (i.e. learning resources, learning methods and activities, learning tools and technologies etc.) based on using pedagogically sound vocabularies of learning components, experts' collective intelligence, and intelligent technologies (e.g. expert evaluation, ontologies, recommender systems, software agents etc.). This pedagogically sound RDF-based personalisation approach is aimed at improving learning quality and effectiveness. 
653 |a Ontology 
653 |a Intelligent systems 
653 |a Students 
653 |a Research 
653 |a Science 
653 |a Learning 
653 |a Cognitive style 
653 |a Semantics 
653 |a Metadata 
653 |a Pedagogy 
653 |a Semantic web 
653 |a Linked Data 
653 |a Recommender systems 
653 |a Knowledge organization 
653 |a Literature reviews 
653 |a Resource Description Framework-RDF 
653 |a Databases 
653 |a Quality standards 
653 |a Educational Resources 
653 |a Learning Processes 
653 |a Educational Technology 
653 |a Educational Needs 
700 1 |a Berniukevièius, Andrius 
700 1 |a Kurilovas, Eugenijus 
773 0 |t European Conference on e-Learning  |g (Oct 2016), p. 323-330 
786 0 |d ProQuest  |t Education Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/1860070014/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/1860070014/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/1860070014/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch