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dc.contributor.authorEr, Erkan 
dc.contributor.authorGómez Sánchez, Eduardo 
dc.contributor.authorDimitriadis Damoulis, Ioannis 
dc.contributor.authorBote Lorenzo, Miguel Luis 
dc.contributor.authorAsensio Pérez, Juan Ignacio 
dc.contributor.authorÁlvarez Álvarez, Susana 
dc.date.accessioned2019-10-17T11:31:59Z
dc.date.available2019-10-17T11:31:59Z
dc.date.issued2019
dc.identifier.citationInteractive Learning Environments Volume 27, 2019 - Issue 5-6: The new potentials for Intelligent Tutoring with learning analyticses
dc.identifier.issn1049-4820es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/38631
dc.descriptionProducción Científicaes
dc.description.abstractThis paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning learning design (LD) and learning analytics (LA) during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past massive open online course, where the learner data and the instructors were accessible for posterior analysis and additional data collection. Through a close collaboration with the instructors, the details of the prediction task were identified, such as the target variable to predict and the practical constraints to consider. Two predictive models were built: LD-specific model (with features based on the LD and pedagogical intentions), and a generic model (with cumulative features, not informed by the LD). Although the LD-specific predictive model did not outperform the generic one, some LD-driven features were powerful. The quantity and the power of such features were associated with the degree to which the students acted as guided by the LD and pedagogical intentions. The leading instructor’s opinion about the importance of the learning activities in the LD was compared with the results of the feature importance analysis. This comparison helped identify the problems in the LD. The implications for improving the LD are discussed.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherTaylor & Francises
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.classificationMOOCes
dc.subject.classificationLearning designes
dc.titleAligning learning design and learning analytics through instructor involvement: a MOOC case studyes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2019 Taylor & Francises
dc.identifier.doi10.1080/10494820.2019.1610455es
dc.relation.publisherversionhttps://www.tandfonline.com/doi/abs/10.1080/10494820.2019.1610455?journalCode=nile20es
dc.identifier.publicationfirstpage685es
dc.identifier.publicationissue5-6es
dc.identifier.publicationlastpage698es
dc.identifier.publicationtitleInteractive Learning Environmentses
dc.identifier.publicationvolume27es
dc.peerreviewedSIes
dc.description.projectMinisterio de Ciencia e Innovación (Proyect grants TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R)es
dc.description.projectJunta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. Project VA257P18)es
dc.description.projectEuropean Commission under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KAes
dc.description.projectEuropean Union’s Horizon 2020 under the Marie Sklodowska-Curie grant agreement 793317es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/793317
dc.identifier.essn1744-5191es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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