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dc.date.accessioned2020-02-27T15:51:07Z
dc.date.available2021-05-17T22:45:47Z
dc.date.created2019-05-21T22:12:11Z
dc.date.issued2019
dc.identifier.citationVanslambrouck, Silke Zhu, Chang Pynoo, Bram Lombaerts, Koen Tondeur, Jo Scherer, Ronny . A latent profile analysis of adult students' online self-regulation in blended learning environments. Computers in Human Behavior. 2019
dc.identifier.urihttp://hdl.handle.net/10852/73393
dc.description.abstractSelf-regulated learning (SRL) is crucial for academic success; therefore support, to enhance and maintain SRL skills is important. In blended adult education, the heterogeneity of adults creates diversity in SRL abilities, which makes it necessary to provide tailored support. Conducting latent profile analyses for a sample of 213 blended adult students, we identified three profiles, namely high, low, and moderate SRL profiles which prove differences in SRL strategy use and imply tailored SRL support. Through multivariate analysis of variance (MANOVA) and multinomial logistic regression, we further explore the differences in SRL between the profiles and the extent to which the students’ personal background characteristics and achievement motivations predict their profile membership. The three profiles differ significantly in terms of the scores of all SRL subscales. Furthermore, only achievement motivation – more specifically, attainment and utility value – predicts profile membership. These results inform educational practice about opportunities for supporting and enhancing SRL skills. Anticipating attainment and utility value, time management, and collaboration with peers are all recommended. More specifically, teachers can, for example, use authentic tasks and examples during the learning process or be a role model regarding online interaction and information sharing.
dc.languageEN
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleA latent profile analysis of adult students' online self-regulation in blended learning environments
dc.typeJournal article
dc.creator.authorVanslambrouck, Silke
dc.creator.authorZhu, Chang
dc.creator.authorPynoo, Bram
dc.creator.authorLombaerts, Koen
dc.creator.authorTondeur, Jo
dc.creator.authorScherer, Ronny
cristin.unitcode185,18,2,0
cristin.unitnameInstitutt for lærerutdanning og skoleforskning
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.cristin1699341
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computers in Human Behavior&rft.volume=&rft.spage=&rft.date=2019
dc.identifier.jtitleComputers in Human Behavior
dc.identifier.volume99
dc.identifier.startpage126
dc.identifier.endpage136
dc.identifier.doihttps://doi.org/10.1016/j.chb.2019.05.021
dc.identifier.urnURN:NBN:no-76548
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn0747-5632
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/73393/2/1-s2.0-S0747563219301967-main.pdf
dc.type.versionAcceptedVersion


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