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dc.contributor.authorKurilov, Jevgenij
dc.date.accessioned2023-09-18T17:45:13Z
dc.date.available2023-09-18T17:45:13Z
dc.date.issued2019
dc.identifier.issn2340-1079
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/125915
dc.description.abstractThe paper aims to analyse application of dedicated psychological questionnaires and educational data mining (EDM) to identify students’ learning styles and thus to create conditions to personalise learning. Dedicated psychological questionnaires could help us to establish individual probabilistic suitability indexes for each analysed student and each learning activity in e.g. Virtual Learning Environment (VLE) to identify which learning activities are the most suitable for particular student. Students’ learning styles-based probabilistic suitability index shows the level of suitability of given learning content, activity or environment to particular student. The higher is probabilistic suitability index the better learning activity fits particular student’s needs. Using appropriate EDM methods and techniques, we could analyse what particular learning activities (and appropriate VLE tools) were practically used by these students earlier, and to what extent. After that, the data on practical use of VLE-based learning activities or tools should be compared with students’ probabilistic suitability indexes. In the case of any noticeable discrepancies, students’ profiles and accompanied probabilistic suitability indexes should be identified more precisely, and students’ personal leaning paths in VLE should be corrected according to new identified data. Thus, using EDM, we could noticeably enhance students’ learning quality and effectiveness. In the paper, first of all, related research review is provided. Second, methodology to personalise learning using both dedicated psychological questionnaires and educational data mining methods and techniques to identify students’ learning styles is presented. Third, some real-life examples of applying both methods using Felder-Silverman Learning Styles Model are presented. The paper is concluded by the statement that the best way to exactly identify students’ learning styles is consistent application of both dedicated psychological questionnaires and educational data mining.eng
dc.formatPDF
dc.format.extentp. 3758-3765
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyConference Proceedings Citation Index - Social Science & Humanities (Web of Science)
dc.relation.isreferencedbyIATED digital library
dc.source.urihttps://library.iated.org/view/KURILOVAS2019APP
dc.source.urihttps://iated.org/inted/
dc.titleApplying dedicated psychological questionnaires VS educational data mining to identify students learning styles
dc.typeStraipsnis konferencijos darbų leidinyje Web of Science DB / Paper in conference publication in Web of Science DB
dcterms.references19
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.researchfieldS 007 - Edukologija / Educology
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.vgtuprioritizedfieldsIK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enlearning personalisation
dc.subject.enlearning styles
dc.subject.endedicated psychological questionnaires
dc.subject.eneducational data mining
dc.subject.enFelder-Silverman Learning Styles Model
dcterms.sourcetitleINTED 2019. 13th international technology, education and development conference, 11th-13th March, 2019, Valencia, Spain : conference proceedings
dc.publisher.nameIATED
dc.publisher.cityValencia
dc.identifier.doi000536018103127
dc.identifier.doi10.21125/inted.2019.0959
dc.identifier.elaba35182676


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