Understanding self-regulation, achievement emotions, and mindset of undergraduates in emergency remote teaching: a latent profile analysis

dc.contributor.authorUslu N.A.
dc.contributor.authorDurak H.Y.
dc.date.accessioned2024-07-22T08:04:55Z
dc.date.available2024-07-22T08:04:55Z
dc.date.issued2022
dc.description.abstractDue to the threat of COVID-19, many educational institutions have made urgent decisions about how to continue teaching and learning, taking their courses online. The transition from face-to-face teaching to Emergency remote teaching (ERT) has made it difficult for individuals to organize their learning processes independently. Therefore, in this period, it is expected that learner profiles will differ from traditional online learning, and there are uncertainties in this regard. The aim of this study is to examine learner profiles in ERT according to online self-regulation (SR) strategies, achievement emotion, and mindset. The study group of this research consists of 659 university students. Latent profile analysis, one-way ANOVA, and multinomial logistic regression analysis (MLA) were used in the analysis of the data. As a result of the research, four profiles emerged: (a) low SR, negative type in emotions, and low growth mindset beliefs, (b) low to moderate SR, positive type in emotion and high growth mindset belief, and (c) moderate to high SR, diversified type in emotion and fixed mindset, (d) high SR, positive type in emotion, growth mindset beliefs. MLA findings show that SR strategies, enjoyment, anxiety, gender, age, and GPA affect differences in predicting several profile memberships of learners. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
dc.identifier.DOI-ID10.1080/10494820.2022.2129391
dc.identifier.issn10494820
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12882
dc.language.isoEnglish
dc.publisherRoutledge
dc.titleUnderstanding self-regulation, achievement emotions, and mindset of undergraduates in emergency remote teaching: a latent profile analysis
dc.typeArticle

Files