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Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?

Recent negotiations of ‘data’ in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers’ digital data. By doing so, the broader debate surrounding t...

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Autor principal: Arantes, Janine Aldous
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047619/
https://www.ncbi.nlm.nih.gov/pubmed/35505893
http://dx.doi.org/10.1007/s13384-022-00530-7
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author_facet Arantes, Janine Aldous
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description Recent negotiations of ‘data’ in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers’ digital data. By doing so, the broader debate surrounding the implications of these technologies and rights within the classroom as a workplace becomes more apparent to practitioners and educational researchers. Drawing on the Australian Human Rights Commission’s Human Rights and Technology final report, this conceptual paper focusses on teachers’ rights alongside emerging technologies that use or provide predictive analytics or artificial intelligence, also called ‘personalisation’. The lens of Postdigital positionality guides the discussion. Three potential consequences are presented as provocations: (1) What might happen if emerging technology uses teachers’ digital data that represent current societal inequality? (2) What might happen if insights provided by such technology are inaccurate, insufficient, or unrepresentative of our teachers? (3) What might happen if the design of the AI system itself is discriminatory? This conceptual paper argues for increased discourse about technologies that use or provide predictive analytics complemented by considering potential consequences associated with algorithmic bias.
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spelling pubmed-90476192022-04-29 Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions? Arantes, Janine Aldous Aust Educ Res Article Recent negotiations of ‘data’ in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers’ digital data. By doing so, the broader debate surrounding the implications of these technologies and rights within the classroom as a workplace becomes more apparent to practitioners and educational researchers. Drawing on the Australian Human Rights Commission’s Human Rights and Technology final report, this conceptual paper focusses on teachers’ rights alongside emerging technologies that use or provide predictive analytics or artificial intelligence, also called ‘personalisation’. The lens of Postdigital positionality guides the discussion. Three potential consequences are presented as provocations: (1) What might happen if emerging technology uses teachers’ digital data that represent current societal inequality? (2) What might happen if insights provided by such technology are inaccurate, insufficient, or unrepresentative of our teachers? (3) What might happen if the design of the AI system itself is discriminatory? This conceptual paper argues for increased discourse about technologies that use or provide predictive analytics complemented by considering potential consequences associated with algorithmic bias. Springer Netherlands 2022-04-28 2023 /pmc/articles/PMC9047619/ /pubmed/35505893 http://dx.doi.org/10.1007/s13384-022-00530-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/)
spellingShingle Article
Arantes, Janine Aldous
Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?
title Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?
title_full Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?
title_fullStr Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?
title_full_unstemmed Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?
title_short Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?
title_sort personalization in australian k-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047619/
https://www.ncbi.nlm.nih.gov/pubmed/35505893
http://dx.doi.org/10.1007/s13384-022-00530-7
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