Show simple item record

dc.contributor.authorPapamitsiou, Zacharoula
dc.contributor.authorEconomides, Anastasios
dc.contributor.authorGiannakos, Michail
dc.date.accessioned2020-01-09T11:59:31Z
dc.date.available2020-01-09T11:59:31Z
dc.date.created2019-12-31T15:56:32Z
dc.date.issued2019
dc.identifier.citationLecture Notes in Computer Science (LNCS). 2019, 11722 423-435.nb_NO
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11250/2635522
dc.description.abstractActivating learners’ deeper thinking mechanisms and reflective judgement (i.e., metacognition) improves learning performance. This study exploits visual analytics to promote metacognition and delivers task-related visualizations to provide on-demand feedback. The goal is to broaden current knowledge on the patterns of on-demand metacognitive feedback usage, with respect to learners’ performance. The results from a between-group and within-group study (N = 174) revealed statistically significant differences on the feedback usage patterns between the performance-based learner clusters. Foremost, the findings shown that learners who consistently request task-related metacognitive feedback and allocate considerable amounts of time on processing it, are more likely to handle task-complexity and cope with conflicting tasks, as well as to achieve high scores. These findings contribute to considering task-related visual analytics as a metacognitive feedback format that facilitates learners’ on-task engagement and data-driven sense-making and increases their awareness of the tasks’ requirements. Implications of the approach are also discussed.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleFostering Learners’ Performance with On-demand Metacognitive Feedbacknb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber423-435nb_NO
dc.source.volume11722nb_NO
dc.source.journalLecture Notes in Computer Science (LNCS)nb_NO
dc.identifier.doi10.1007/978-3-030-29736-7_32
dc.identifier.cristin1764571
dc.description.localcodeThis is a post-peer-review, pre-copyedit version of an article. Locked until 9.9.2020 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-29736-7_32nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record