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dc.contributor.authorMisiejuk, Kamila
dc.contributor.authorWasson, Barbara
dc.contributor.authorEgelandsdal, Kjetil
dc.date.accessioned2021-07-02T09:37:44Z
dc.date.available2021-07-02T09:37:44Z
dc.date.created2020-12-14T20:31:28Z
dc.date.issued2021
dc.identifier.issn0747-5632
dc.identifier.urihttps://hdl.handle.net/11250/2763075
dc.description.abstractPeer assessment (PA) is the process of students grading and giving feedback to each other’s work. Learning analytics is a field focused on analysing educational data to understand and improve learning processes. Using learning analytics on PA data has the potential to gain new insights into the feedback giving/receiving process. This exploratory study focuses on backward evaluation, an under researched aspect of peer assessment, where students react to the feedback that they received on their work. Two aspects are analysed: 1) backward evaluation characteristics depending on student perception of feedback that they receive on their work, and 2) the relationship between rubric characteristics and backward evaluation. A big dataset (N=7,660 records) from an online platform called Peergrade was analysed using both statistical methods and Epistemic Network Analysis. Students who found feedback useful tended to be more accepting by acknowledging their errors, intending to revise their text, and praising its usefulness, while students who found the feedback less useful tended to be more defensive by expressing that they were confused about its meaning, critical towards its form and focus, and in disagreement with the claims. Moreover, students mostly suggested feedback improvement in terms of feedback specificity, justification and constructivity, rather than kindness. The paper concludes by discussing the potential and limitations of using LA methods to analyse big PA datasets.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0747563220304052?via%3Dihub
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleUsing learning analytics to understand student perceptions of peer feedbacken_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright the authors 2020en_US
dc.source.articlenumber106658en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1016/j.chb.2020.106658
dc.identifier.cristin1859740
dc.source.journalComputers in Human Behavioren_US
dc.identifier.citationComputers in Human Behavior. 2021, 117, 106658.en_US
dc.source.volume117en_US


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