OnTask: Delivering Data-Informed, Personalized Learning Support Actions

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dc.contributor.author Pardo, A en
dc.contributor.author Bartimote, K en
dc.contributor.author Buckingham Shum, S en
dc.contributor.author Dawson, S en
dc.contributor.author Gao, J en
dc.contributor.author Gašević, D en
dc.contributor.author Leichtweis, Steven en
dc.contributor.author Liu, D en
dc.contributor.author Martínez-Maldonado, R en
dc.contributor.author Mirriahi, N en
dc.contributor.author Moskal, A en
dc.contributor.author Schulte, J en
dc.contributor.author Siemens, G en
dc.contributor.author Vigentini, L en
dc.date.accessioned 2019-03-11T21:09:18Z en
dc.date.issued 2018-12-11 en
dc.identifier.citation Journal of Learning Analytics 5(3):235-249 11 Dec 2018 en
dc.identifier.uri http://hdl.handle.net/2292/45925 en
dc.description.abstract The learning analytics community has matured significantly over the past few years as a middle space where technology and pedagogy combine to support learning experiences. To continue to grow and connect these perspectives, research needs to move beyond the level of basic support actions. This means exploring the use of data to prove richer forms of actions, such as personalized feedback, or hybrid approaches where instructors interpret the outputs of algorithms and select an appropriate course of action. This paper proposes the following three contributions to connect data extracted from the learning experience with such personalized student support actions: 1) a student–instructor centred conceptual model connecting a representation of the student information with a basic set of rules created by instructors to deploy Personalized Learning Support Actions (PLSAs); 2) a software architecture based on this model with six categories of functional blocks to deploy the PLSAs; and 3) a description of the implementation of this architecture as an open-source platform to promote the adoption and exploration of this area. en
dc.relation.ispartofseries Journal of Learning Analytics en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.title OnTask: Delivering Data-Informed, Personalized Learning Support Actions en
dc.type Journal Article en
dc.identifier.doi 10.18608/jla.2018.53.15 en
pubs.issue 3 en
pubs.begin-page 235 en
pubs.volume 5 en
dc.rights.holder Copyright: The authors en
pubs.author-url https://learning-analytics.info/journals/index.php/JLA/article/view/5988 en
pubs.end-page 249 en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Article en
pubs.elements-id 757822 en
dc.identifier.eissn 1929-7750 en
pubs.record-created-at-source-date 2018-12-12 en
pubs.online-publication-date 2018-12-11 en

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