Learning analytics dashboard: a tool for providing actionable insights to learners

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dc.citation.issue1
dc.citation.volume19
dc.contributor.authorSusnjak T
dc.contributor.authorRamaswami G
dc.contributor.authorMathrani A
dc.date.accessioned2023-11-20T01:37:37Z
dc.date.available2022-02-14
dc.date.available2021-12-14
dc.date.available2023-11-20T01:37:37Z
dc.date.issued2022-02-14
dc.description.abstractThis study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.
dc.description.publication-statusPublished
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000754664100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifierARTN 12
dc.identifier.citationINTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION, 2022, 19 (1)
dc.identifier.doi10.1186/s41239-021-00313-7
dc.identifier.elements-id451083
dc.identifier.harvestedMassey_Dark
dc.identifier.issn2365-9440
dc.identifier.urihttps://hdl.handle.net/10179/16907
dc.publisherBioMed Central Ltd
dc.relation.isPartOfINTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION
dc.relation.urihttps://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-021-00313-7
dc.rightsCC BY
dc.rights(c) The authors
dc.subjectDashboard
dc.subjectLearner analytics
dc.subjectActionable insights
dc.subjectModel interpretability
dc.subjectExplainable AI
dc.subjectCounterfactuals
dc.titleLearning analytics dashboard: a tool for providing actionable insights to learners
dc.typeJournal article
massey.relation.uri-descriptionPublished version
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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