Please use this identifier to cite or link to this item: https://digital.fundacionceibal.edu.uy/jspui/handle/123456789/284
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dc.contributor.authorCORMACK, Andrew Nicholas-
dc.date.accessioned2018-11-30T17:08:52Z-
dc.date.available2018-11-30T17:08:52Z-
dc.date.issued2016-
dc.identifier.citationCormack, A. N. (2016). A Data Protection Framework for Learning Analytics. Journal of Learning Analytics, 3(1), 91-106. Website https://learning-analytics.info/journals/index.php/JLA/article/view/4554 (accessed November 30th, 2018).es_ES
dc.identifier.urihttps://digital.fundacionceibal.edu.uy/jspui/handle/123456789/284-
dc.descriptionLearning analytics, privacy, data protection, consent, legitimate interestses_ES
dc.description.abstractMost studies on the use of digital student data adopt an ethical framework derived from human-studies research, based on the informed consent of the experimental subject. However consent gives universities little guidance on the use of learning analytics as a routine part of educational provision: which purposes are legitimate and which analyses involve an unacceptable risk of harm. Obtaining consent when students join a course will not give them meaningful control over their personal data three or more years later. Relying on consent may exclude those most likely to benefit from early interventions. This paper proposes an alternative framework based on European Data Protection law. Separating the processes of analysis (pattern-finding) and intervention (pattern-matching) gives students and staff continuing protection from inadvertent harm during data analysis; students have a fully informed choice whether or not to accept individual interventions; organisations obtain clear guidance: how to conduct analysis, which analyses should not proceed, and when and how interventions should be offered. The framework provides formal support for practices that are already being adopted and helps with several open questions in learning analytics, including its application to small groups and alumni, automated processing and privacy-sensitive data.es_ES
dc.format.extentpp. 91-106es_ES
dc.language.isoenes_ES
dc.publisherSOLAR (Society for Learning Analytics Research)es_ES
dc.relation.ispartofJournal of Learning Analyticses_ES
dc.relation.ispartofseriesVol. 3es_ES
dc.relation.ispartofseriesN° 1es_ES
dc.subjectEDUCATIONes_ES
dc.subjectEDUCACIÓNes_ES
dc.subjectPRIVACYes_ES
dc.subjectPRIVACIDADes_ES
dc.subjectETHICSes_ES
dc.subjectÉTICAes_ES
dc.subjectTECHNOLOGYes_ES
dc.subjectTECNOLOGÍAes_ES
dc.titleA Data Protection Framework for Learning Analyticses_ES
dc.typeOriginal journal articlees_ES
dc.description.versionPublishedes_ES
dc.identifier.doihttps://doi.org/10.18608/jla.2016.31.6-
dc.subject.researchlineResources and Platformses_ES
dc.subject.researchlineNew Ways of Knowing, Learning, Teaching and Evaluatees_ES
dc.subject.researchlineOtheres_ES
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