dc.contributor.author | Mangaroska, Katerina | |
dc.contributor.author | Giannakos, Michail | |
dc.date.accessioned | 2019-01-29T10:02:18Z | |
dc.date.available | 2019-01-29T10:02:18Z | |
dc.date.created | 2018-10-12T10:26:04Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 1939-1382 | |
dc.identifier.uri | http://hdl.handle.net/11250/2582773 | |
dc.description.abstract | As the fields of learning analytics and learning design mature, the convergence and synergies between these two fields became an important area for research. This paper intends to summarize the main outcomes of a systematic literature review of empirical evidence on learning analytics for learning design. Moreover, this paper presents an overview of what and how learning analytics have been used to inform learning design decisions and in what contexts. The search was performed in seven academic databases, resulting in 43 papers included in the main analysis. The results from the review depict the ongoing design patterns and learning phenomena that emerged from the synergy that learning analytics and learning design impose on the current status of learning technologies. Finally, this review stresses that future research should consider developing a framework on how to capture and systematize learning design data grounded in learning analytics and learning theory, and document what learning design choices made by educators influence subsequent learning activities and performances over time. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.title | Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning | nb_NO |
dc.title.alternative | Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.journal | IEEE Transactions on Learning Technologies | nb_NO |
dc.identifier.doi | 10.1109/TLT.2018.2868673 | |
dc.identifier.cristin | 1619913 | |
dc.relation.project | Norges forskningsråd: 255129 | nb_NO |
dc.description.localcode | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |