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dc.contributor.authorHansen, Cecilie
dc.contributor.authorNetteland, Grete
dc.contributor.authorWasson, Barbara
dc.date.accessioned2017-02-09T08:43:57Z
dc.date.available2017-02-09T08:43:57Z
dc.date.issued2016
dc.identifier.citationCEUR Workshop Proceedings, 2016, 1601, 87-90.nb_NO
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/11250/2430060
dc.description.abstractOne challenge faced by workplaces is having enough relevant data to support datadriven decision making related to the further education/training of their employees, to identify competence gaps within their work force, and to contribute to organisational learning. We are propose that data driven decision-making can be supported by the combination of competence mapping, collection of assessment and performance data from various sources, learning analytics of the data, and visualisation of the results in an open learner model (OLM). In the iComPAss project we work with two learning scenarios, health workers studying for a Masters in health leadership, and the on-the-job training of firefighters to study this challenge. The project builds on and extends ideas from the former EU-project Adapt-It, and on a learning analytics approach and an OLM developed in the EU NEXT-TELL project. This paper elaborates on our approach.nb_NO
dc.language.isoengnb_NO
dc.publisherTechnical University of Aachennb_NO
dc.relation.urihttp://nbn-resolving.de/urn:nbn:de:0074-1601-3
dc.subjectLearning analyticsnb_NO
dc.subjectCompetence developmentnb_NO
dc.subject4C/IDnb_NO
dc.subjectOpen learner modelnb_NO
dc.subjectVisualizationnb_NO
dc.titleLearning analytics and open learning modelling for professional competence development of firefighters and future healthcare leadersnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.pagenumber87-90nb_NO
dc.source.volume1601nb_NO
dc.source.journalCEUR Workshop Proceedingsnb_NO
dc.identifier.cristin1378603
dc.relation.projectNorges forskningsråd: 246765nb_NO
dc.identifier.urnhttp://nbn-resolving.de/urn:nbn:de:0074-1601-3
dc.identifier.urn


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