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dc.contributor.authorMamčenko, Jelena
dc.contributor.authorKurilov, Jevgenij
dc.date.accessioned2023-09-18T17:01:23Z
dc.date.available2023-09-18T17:01:23Z
dc.date.issued2017
dc.identifier.issn2049-100X
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/119034
dc.description.abstractThe paper aims to analyse application of learning analytics (LA) to support learning personalisation in virtual learning environments, namely Moodle. In the paper, first of all, literature review was performed on LA methods and techniques used to personalise students’ e-learning activities. Literature review has revealed that LA are known as the measurement, collection, analysis, and reporting of data about learners and their contexts to understand and optimise learning and environments in which it occurs. In the paper, an original methodology to personalise learning is presented. Second, existing Moodle-based learning activities and tools were interlinked with students’ learning styles according to Felder-Silverman learning styles model using expert evaluation method. Third, a group of students was analysed to identify their individual learner profiles, and probabilistic suitability indexes were calculated for each analysed student and each Moodle-based learning activity to identify which learning activities or tools are the most suitable for particular student. The higher is suitability index the better learning activity or tool fits particular student’s needs. Fourth, using appropriate LA methods and techniques, we could analyse what particular learning activities or tools were practically used by these students in Moodle, and to what extent. Fifth, the data on practical use of Moodle-based learning activities or tools should be compared with students’ suitability indexes. In the case of any noticeable discrepancies, students’ profiles and accompanied suitability indexes should be identified more precisely, and students’ personal leaning paths in Moodle should be corrected according to new identified data. Thus, using LA, we could noticeably enhance students’ learning quality and effectiveness.eng
dc.formatPDF
dc.format.extentp. 353-361
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyConference Proceedings Citation Index - Social Science & Humanities (Web of Science)
dc.subjectIK01 - Informacinės technologijos, ontologinės ir telematikos sistemos / Information technologies, ontological and telematic systems
dc.titleOn using learning analytics to personalise learning in virtual learning environments
dc.typeStraipsnis konferencijos darbų leidinyje Web of Science DB / Paper in conference publication in Web of Science DB
dcterms.references36
dc.type.pubtypeP1a - Straipsnis konferencijos darbų leidinyje Web of Science DB / Article in conference proceedings Web of Science DB
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.researchfieldS 004 - Ekonomika / Economics
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enLearning analytics
dc.subject.enE-leaning
dc.subject.enLearning personalisation
dc.subject.enLearning styles
dc.subject.enVirtual learning environments
dc.subject.enExpert evaluation
dcterms.sourcetitleECEL 2017. Proceedings of the 16th European Conference on e-Learning, hosted by ISCAP Polytechnic of Porto Portugal, 26-27 October 2017
dc.publisher.nameAcademic Conferences and Publishing International (ACPI)
dc.publisher.cityReading
dc.identifier.doi000457842600045
dc.identifier.doi2-s2.0-85037546395
dc.identifier.elaba24601842


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