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dc.contributor.authorSlade, Sharon
dc.contributor.authorPrinsloo, Paul
dc.contributor.authorKhalil, Mohammad
dc.date.accessioned2024-09-04T13:32:57Z
dc.date.available2024-09-04T13:32:57Z
dc.date.created2023-11-09T12:06:58Z
dc.date.issued2023
dc.identifier.issn2398-5348
dc.identifier.urihttps://hdl.handle.net/11250/3150177
dc.description.abstractPurpose: The purpose of this paper is to explore and establish the contours of trust in learning analytics and to establish steps that institutions might take to address the “trust deficit” in learning analytics. Design/methodology/approach: “Trust” has always been part and parcel of learning analytics research and practice, but concerns around privacy, bias, the increasing reach of learning analytics, the “black box” of artificial intelligence and the commercialization of teaching and learning suggest that we should not take stakeholder trust for granted. While there have been attempts to explore and map students’ and staff perceptions of trust, there is no agreement on the contours of trust. Thirty-one experts in learning analytics research participated in a qualitative Delphi study. Findings: This study achieved agreement on a working definition of trust in learning analytics, and on factors that impact on trusting data, trusting institutional understandings of student success and the design and implementation of learning analytics. In addition, it identifies those factors that might increase levels of trust in learning analytics for students, faculty and broader. Research limitations/implications: The study is based on expert opinions as such there is a limitation of how much it is of a true consensus. Originality/value: Trust cannot be assumed is taken for granted. This study is original because it establishes a number of concerns around the trustworthiness of learning analytics in respect of how data and student learning journeys are understood, and how institutions can address the “trust deficit” in learning analytics.en_US
dc.language.isoengen_US
dc.publisherEmeralden_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.title“Trust us,” they said. Mapping the contours of trustworthiness in learning analyticsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 the authorsen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1108/ILS-04-2023-0042
dc.identifier.cristin2194500
dc.source.journalInformation and Learning Science (ILS)en_US
dc.source.pagenumber306-325en_US
dc.identifier.citationInformation and Learning Science (ILS). 2023, 124 (9/10), 306-325.en_US
dc.source.volume124en_US
dc.source.issue9/10en_US


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