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dc.contributor.authorZhidkikh, Denis
dc.contributor.authorSaarela, Mirka
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2023-06-19T09:13:27Z
dc.date.available2023-06-19T09:13:27Z
dc.date.issued2023
dc.identifier.citationZhidkikh, D., Saarela, M., & Kärkkäinen, T. (2023). Measuring self‐regulated learning in a junior high school mathematics classroom : Combining aptitude and event measures in digital learning materials. <i>Journal of Computer Assisted Learning</i>, <i>39</i>(6), 1834-1851. <a href="https://doi.org/10.1111/jcal.12842" target="_blank">https://doi.org/10.1111/jcal.12842</a>
dc.identifier.otherCONVID_183628217
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/87918
dc.description.abstractBackground Measurement of students' self-regulation skills is an active topic in education research, as effective assessment helps devising support interventions to foster academic achievement. Measures based on event tracing usually require large amounts of data (e.g., MOOCs and large courses), while aptitude measures are often qualitative and need careful interpretation. Precise and interpretable evaluation of self-regulation skills in a normal K-12 classroom thus poses a challenge. Objectives The present study proposes and explores a learning analytics method of combining aptitude and event measures to evaluate student's self-regulation skills. Methods An explorative learning analytics study was conducted in a junior high school mathematics class (N = 20 students), using a three-lesson intervention with digital learning materials. Students first assessed their self-regulation skills with a self-report questionnaire, after which trace logs and observations of student behaviour were collected. Learning sessions were extracted from trace logs, clustered, and linked to learning strategies. Students were clustered by the self-report results and learning behaviour profiles. Session clusters, student behaviour clusters and assignment grades were also tested for association. Results and Conclusions The detected session and student behaviour types were linked to learning tactics and strategies found in prior studies. Additionally, association was found between self-reported self-regulation skills and the student behaviour obtained from trace logs. Implications The results demonstrate the feasibility of concurrently using aptitude and event measures on a classroom scale, providing teachers with a tool to evaluate and support self-regulated learning. Combined with further measures like predictive learning analytics, teachers can obtain an early and highly interpretable picture of at-risk students in their classes.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofseriesJournal of Computer Assisted Learning
dc.rightsCC BY 4.0
dc.subject.otheraptitude measures
dc.subject.otherevent measures
dc.subject.otherjunior high school
dc.subject.otherlearning analytics
dc.subject.othermathematics
dc.subject.otherself-regulated learning
dc.titleMeasuring self‐regulated learning in a junior high school mathematics classroom : Combining aptitude and event measures in digital learning materials
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202306193973
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineComputing Education Researchfi
dc.contributor.oppiaineHuman and Machine based Intelligence in Learningfi
dc.contributor.oppiaineTutkintokoulutusfi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineTekniikkafi
dc.contributor.oppiaineKoulutusteknologia ja kognitiotiedefi
dc.contributor.oppiaineResurssiviisausyhteisöfi
dc.contributor.oppiaineComputing Education Researchen
dc.contributor.oppiaineHuman and Machine based Intelligence in Learningen
dc.contributor.oppiaineDegree Educationen
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineEngineeringen
dc.contributor.oppiaineLearning and Cognitive Sciencesen
dc.contributor.oppiaineSchool of Resource Wisdomen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1834-1851
dc.relation.issn0266-4909
dc.relation.numberinseries6
dc.relation.volume39
dc.type.versionpublishedVersion
dc.rights.copyright© 2023 The Authors. Journal of Computer Assisted Learning published by John Wiley & Sons Ltd.
dc.rights.accesslevelopenAccessfi
dc.subject.ysoyläkoulu
dc.subject.ysooppiminen
dc.subject.ysoarviointi
dc.subject.ysoyläkoululaiset
dc.subject.ysoitsesäätely (psykologia)
dc.subject.ysodigitaalinen oppimateriaali
dc.subject.ysomittaus
dc.subject.ysooppilaat
dc.subject.ysomatematiikka
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21292
jyx.subject.urihttp://www.yso.fi/onto/yso/p2945
jyx.subject.urihttp://www.yso.fi/onto/yso/p7413
jyx.subject.urihttp://www.yso.fi/onto/yso/p38262
jyx.subject.urihttp://www.yso.fi/onto/yso/p14462
jyx.subject.urihttp://www.yso.fi/onto/yso/p3597
jyx.subject.urihttp://www.yso.fi/onto/yso/p4794
jyx.subject.urihttp://www.yso.fi/onto/yso/p8131
jyx.subject.urihttp://www.yso.fi/onto/yso/p3160
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1111/jcal.12842
dc.type.okmA1


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