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dc.contributor.authorLiu, Qinyi
dc.contributor.authorKhalil, Mohammad
dc.date.accessioned2023-10-30T14:20:55Z
dc.date.available2023-10-30T14:20:55Z
dc.date.created2023-09-23T18:34:07Z
dc.date.issued2023
dc.identifier.issn0007-1013
dc.identifier.urihttps://hdl.handle.net/11250/3099494
dc.description.abstractThe field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.titleUnderstanding privacy and data protection issues in learning analytics using a systematic reviewen_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.doi10.1111/bjet.13388
dc.identifier.cristin2178263
dc.source.journalBritish Journal of Educational Technology (BJET)en_US
dc.source.pagenumber1715-1747en_US
dc.identifier.citationBritish Journal of Educational Technology (BJET). 2023, 54 (6), 1715-1747.en_US
dc.source.volume54en_US
dc.source.issue6en_US


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Navngivelse-Ikkekommersiell 4.0 Internasjonal
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