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Measuring self-regulated learning and online learning events to predict student academic performance

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dc.title Measuring self-regulated learning and online learning events to predict student academic performance en
dc.contributor.author Vaculíková, Jitka
dc.relation.ispartof Studia Paedagogica
dc.identifier.issn 1803-7437 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2018
utb.relation.volume 23
utb.relation.issue 4
dc.citation.spage 91
dc.citation.epage 118
dc.type article
dc.language.iso en
dc.publisher Masarykova univerzita, Filozofická fakulta
dc.identifier.doi 10.5817/SP2018-4-5
dc.relation.uri https://www.phil.muni.cz/journals/index.php/studia-paedagogica/article/view/1870
dc.subject Academic performance en
dc.subject Computer-Assisted learning en
dc.subject Online learning events en
dc.subject Self-regulated learning en
dc.description.abstract The aim of this study is to identify whether the combination of self-reported data that measure self-regulated learning (SRL) and computer-Assisted data that capture student engagement with an online learning environment could be used to predict student academic achievement. Personally engaged study strategies focused on deep-level learning, the process of taking control, and the evaluation of students' own learning characterize SRL. Diverse theories on how students benefit from SRL underline its positive impact on student academic outcomes. Similarly, there is no doubt that the future trend in education leans towards the integration of technolog y into teaching in order to exploit its full potential. To benefit from both approaches, a combination of self-reported data and detailed online learning events obtained from an online learning environment were investigated in relation to their ability to predict student academic achievement. A case study of 54 university students enrolled in a blended-learning course showed that of the tested SRL variables and observed learning activities, student interaction with auxiliary materials that were part of the course helped to predict academic outcomes. Despite the relatively low ability of the model to explain why some students were able to become successful learners, the presented results highlight the importance of analysing online learning events in computer-Assisted teaching and learning. © 2018 Masaryk University, Faculty of Arts. All rights reserved. en
utb.faculty Faculty of Humanities
dc.identifier.uri http://hdl.handle.net/10563/1008529
utb.identifier.obdid 43878823
utb.identifier.scopus 2-s2.0-85061615231
utb.source j-scopus
dc.date.accessioned 2019-07-08T11:59:53Z
dc.date.available 2019-07-08T11:59:53Z
dc.rights Attribution 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Vaculíková, Jitka
utb.fulltext.affiliation JITKA VACULÍKOVÁ Research Centre, Tomas Bata University in Zlín, Zlín, Czech Republic E-mail: vaculikova@utb.cz
utb.fulltext.dates -
utb.scopus.affiliation Research Centre, Tomas Bata University in Zlín, Zlín, Czech Republic
utb.fulltext.faculty Faculty of Humanities
utb.fulltext.ou Research Centre
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Attribution 4.0 International Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je Attribution 4.0 International