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dc.contributor.authorKurilov, Jevgenij
dc.contributor.authorDagienė, Valentina
dc.date.accessioned2023-09-18T19:24:23Z
dc.date.available2023-09-18T19:24:23Z
dc.date.issued2010
dc.identifier.issn1479-4403
dc.identifier.other(BIS)VGT02-000025679
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/139061
dc.description.abstractThe main research object of the paper is investigation and proposal of the comprehensive Learning Object Repositories (LORs) quality evaluation tool suitable for their multiple criteria decision analysis, evaluation and optimisation. Both LORs 'internal quality' and 'quality in use' evaluation (decision making) criteria are analysed in the paper. The authors have analysed several well-known LORs quality evaluation tools. In their opinion, the comprehensive multiple criteria LOR quality evaluation tool should include both general software 'internal quality' evaluation criteria and 'quality in use' evaluation criteria suitable for the particular project or user. In the authors' opinion, the proposed LOR 'Architecture' group criteria are general 'internal quality' evaluation criteria, and 'Metadata', 'Storage', 'Graphical user interface' and 'Other' are 'customisable' 'quality in use' evaluation criteria. The authors have also presented their comprehensive Virtual Learning Environments (VLEs) quality evaluation tool combining both 'internal quality' (i.e., 'General Architecture') and 'quality in use' (i.e., 'Adaptation') technological evaluation criteria. The authors have proposed to use the quality evaluation rating tool while evaluating LORs and VLEs. The authors have analysed that if we want to optimise LORs and VLEs (or the other learning software packages) for the individual learner needs, i.e., to personalise his/her learning process in the best way according to their prerequisites, preferred learning speed and methods etc., we should use the experts' additive utility function including the proposed LORs and VLEs expert evaluation criteria ratings together with the experts preferred weights of evaluation criteria. In this case we have the multiple criteria optimisation task using criteria ratings, and their weights. Quality evaluation criteria of the main e-Learning system componenteng
dc.format.extentp. 141-151
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyINSPEC
dc.relation.isreferencedbyEbsco (nenaudotinas)
dc.relation.isreferencedbyScopus
dc.source.urihttp://www.ejel.org/volume8/issue2
dc.source.urihttp://www.ejel.org/Volume-8/v8-i2/v8-i2-art-10.htm
dc.titleMultiple criteria evaluation of quality and optimisation of e-learning system components
dc.typeStraipsnis konferencijos darbų leidinyje Scopus DB / Paper in conference publication in Scopus DB
dcterms.references18
dc.type.pubtypeP1b - Straipsnis konferencijos darbų leidinyje Scopus DB / Article in conference proceedings Scopus DB
dc.contributor.institutionVilniaus universitetas Vilniaus Gedimino technikos universitetas Matematikos ir informatikos institutas
dc.contributor.institutionMatematikos ir informatikos institutas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldS 007 - Edukologija / Educology
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.enManaging quality in e-leaming
dc.subject.enMultiple criteria evaluation
dc.subject.enLearning object repositories
dc.subject.enVirtual learning environments
dc.subject.enOptimisation
dcterms.sourcetitleElectronic journal of e-learning (EJEL) : special issue ECEL 2009
dc.description.issueiss. 2
dc.description.volumeVol. 8
dc.publisher.nameAcademic Publishing Limited
dc.publisher.cityHamphsire
dc.identifier.doiLBT02-000039188
dc.identifier.elaba4005677


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