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dc.contributor.authorPapamitsiou, Zacharoula
dc.contributor.authorGiannakos, Michail
dc.contributor.authorOchoa, Xavier
dc.date.accessioned2021-02-18T13:09:06Z
dc.date.available2021-02-18T13:09:06Z
dc.date.created2021-01-04T15:44:19Z
dc.date.issued2020
dc.identifier.isbn978-1-4503-7712-6
dc.identifier.urihttps://hdl.handle.net/11250/2728954
dc.description.abstractThis study aims to identify the conceptual structure and the thematic progress in Learning Analytics (evolution) and to elaborate on backbone/emerging topics in the field (maturity) from 2011 to September 2019. To address this objective, this paper employs hierarchical clustering, strategic diagrams and network analysis to construct the intellectual map of the Learning Analytics community and to visualize the thematic landscape in this field, using co-word analysis. Overall, a total of 459 papers from the proceedings of the Learning Analytics and Knowledge (LAK) conference and 168 articles published in the Journal of Learning Analytics (JLA), and the respective 3092 author-assigned keywords and 4051 machine-extracted key-phrases, were included in the analyses. The results indicate that the community has significantly focused in areas like Massive Open Online Courses and visualizations; Learning Management Systems, assessment and self-regulated learning are also basic topics, yet topics like natural language processing and orchestration are emerging. The analysis highlights the shift of the research interest throughout the past decade, and the rise of new topics, comprising evidence that the field is expanding. Limitations of the approach and future work plans conclude the paper.en_US
dc.language.isoengen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.ispartofLAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
dc.titleFrom childhood to maturity: Are we there yet? Mapping the intellectual progress in learning analytics during the past decadeen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber559-568en_US
dc.identifier.doihttps://dl.acm.org/doi/10.1145/3375462.3375519
dc.identifier.cristin1865050
dc.description.localcode© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here http://dx.doi.org/https://doi.org/10.1145/3375462.3375519en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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