DC FieldValueLanguage
dc.contributor.authorGiannakopoulos, Georgios-
dc.contributor.authorDrivas, Ioannis-
dc.contributor.authorSakas, Damianos-
dc.date.accessioned2023-10-14T13:45:27Z-
dc.date.available2023-10-14T13:45:27Z-
dc.date.issued2020-01-01-
dc.identifier.isbn9783030496623-
dc.identifier.issn16113349-
dc.identifier.issn03029743-
dc.identifier.other85086277920-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/347-
dc.description.abstractThe untamed big data era raises opportunities in learning analytics sector for the provision of enhanced educational material to learners. Nevertheless, big data analytics, brings big troubles in exploration, validation and predictive model development. In this paper, the authors present a data-driven methodology for greater utilization of learning analytics datasets, with the purpose to improve the knowledge of instructors about learners performance and provide better personalization with optimized intelligent tutoring systems. The proposed methodology is unfolded in three stages. First, the learning analytics summarization for initial exploratory purposes of learners experience and their behavior in e-learning environments. Subsequently, the exploration of possible interrelationships between metrics and the validation of the proposed learning analytics schemas, takes place. Lastly, the development of predictive models and simulation both on an aggregated and micro-level perspective through agent-based modeling is recommended, with the purpose to reinforce the feedback for instructors and intelligent tutoring systems. The study contributes to the knowledge expansion both for researchers and practitioners with the purpose to optimize the provided online learning experience.en_US
dc.language.isoenen_US
dc.relation.ispartofIntelligent Tutoring Systemsen_US
dc.relation.ispartofseriesLecture Notes in Computer Scienceen_US
dc.subjectBig dataen_US
dc.subjectE-learningen_US
dc.subjectIntelligent tutoring systemsen_US
dc.subjectLearning analyticsen_US
dc.subjectLearning management systemsen_US
dc.subjectMethodsen_US
dc.subjectOnline learning platformsen_US
dc.titleLearning analytics in big data era. Exploration, validation and predictive models developmenten_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Intelligent Tutoring Systemsen_US
dc.identifier.doi10.1007/978-3-030-49663-0_50en_US
dc.identifier.scopus2-s2.0-85086277920-
dc.relation.deptDepartment of Archival, Library and Information Studiesen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume12149 LNCSen_US
dc.identifier.spage407en_US
dc.identifier.epage410en_US
dc.linkhttps://link.springer.com/chapter/10.1007/978-3-030-49663-0_50en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldSocial Sciencesen_US
dc.countryGreeceen_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-1659-3504-
crisitem.author.orcid0000-0003-2407-9502-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου
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