dc.contributor.author | Papamitsiou, Zacharoula | |
dc.contributor.author | Economides, Anastasios | |
dc.contributor.author | Giannakos, Michail | |
dc.date.accessioned | 2020-01-09T11:59:31Z | |
dc.date.available | 2020-01-09T11:59:31Z | |
dc.date.created | 2019-12-31T15:56:32Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Lecture Notes in Computer Science (LNCS). 2019, 11722 423-435. | nb_NO |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/11250/2635522 | |
dc.description.abstract | Activating 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.iso | eng | nb_NO |
dc.publisher | Springer Verlag | nb_NO |
dc.title | Fostering Learners’ Performance with On-demand Metacognitive Feedback | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 423-435 | nb_NO |
dc.source.volume | 11722 | nb_NO |
dc.source.journal | Lecture Notes in Computer Science (LNCS) | nb_NO |
dc.identifier.doi | 10.1007/978-3-030-29736-7_32 | |
dc.identifier.cristin | 1764571 | |
dc.description.localcode | This 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_32 | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |