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dc.contributor.authorWebb, ME
dc.contributor.authorFluck, A
dc.contributor.authorMagenheim, J
dc.contributor.authorMalyn-Smith, J
dc.contributor.authorWaters, J
dc.contributor.authorDeschênes, M
dc.contributor.authorZagami, J
dc.date.accessioned2020-11-26T03:36:23Z
dc.date.available2020-11-26T03:36:23Z
dc.date.issued2020
dc.identifier.issn1042-1629
dc.identifier.doi10.1007/s11423-020-09858-2
dc.identifier.urihttp://hdl.handle.net/10072/399689
dc.description.abstractMachine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofjournalEducational Technology Research and Development
dc.subject.fieldofresearchSpecialist studies in education
dc.subject.fieldofresearchcode3904
dc.titleMachine learning for human learners: opportunities, issues, tensions and threats
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationWebb, ME; Fluck, A; Magenheim, J; Malyn-Smith, J; Waters, J; Deschênes, M; Zagami, J, Machine learning for human learners: opportunities, issues, tensions and threats, Educational Technology Research and Development, 2020
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-11-26T03:27:38Z
dc.description.versionVersion of Record (VoR)
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.
gro.rights.copyright© The Author(s), 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorZagami, Jason A.
dc.subject.socioeconomiccode1601 Learner and learning


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