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Primary students’ learning about citizenship through data science

Much of the mathematics that children experience in school neglect the skills increasingly needed for citizenship, particularly the power of complex data to investigate and make sense of the world. We draw on the relatively new field of data science as a multi-disciplinary approach to investigate pr...

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Detalles Bibliográficos
Autores principales: Makar, Katie, Fry, Kym, English, Lyn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9806803/
https://www.ncbi.nlm.nih.gov/pubmed/36619684
http://dx.doi.org/10.1007/s11858-022-01450-7
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author Makar, Katie
Fry, Kym
English, Lyn
author_facet Makar, Katie
Fry, Kym
English, Lyn
author_sort Makar, Katie
collection PubMed
description Much of the mathematics that children experience in school neglect the skills increasingly needed for citizenship, particularly the power of complex data to investigate and make sense of the world. We draw on the relatively new field of data science as a multi-disciplinary approach to investigate problems through analysis of massive, non-standard, incongruous and/or messy data. Our exploratory qualitative study had as its research question: What can children learn about citizenship when they engage with data science? The case study in this paper illustrated ways that children’s learning about citizenship were enriched through an age-appropriate data science investigation. The study analysed classroom video from a Year 4 classroom (aged 9–10) over six lessons that integrated curricula in digital technologies, health, and mathematics. In these lessons, the children generated and analysed non-standard data and debated social, well-being and privacy issues as they considered their activities in cyberspace. The video data were analysed using a framework based on critical citizenship education literature that examined dimensions of power, collective engagement, individual responsibility and action. Three key findings emerged. First, the case study highlighted skills in citizenship education developed through data science, positioning children as agents and advocates. Second, the study showed how a complex data investigation in citizenship education was achievable with primary children through meaningful curriculum integration. This is important given that problems that citizens address are typically interdisciplinary. Finally, the findings revealed a gap between data science skills and those developed in the mathematics curriculum, and recommend ways that the maths curriculum could be updated.
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spelling pubmed-98068032023-01-04 Primary students’ learning about citizenship through data science Makar, Katie Fry, Kym English, Lyn ZDM Original Paper Much of the mathematics that children experience in school neglect the skills increasingly needed for citizenship, particularly the power of complex data to investigate and make sense of the world. We draw on the relatively new field of data science as a multi-disciplinary approach to investigate problems through analysis of massive, non-standard, incongruous and/or messy data. Our exploratory qualitative study had as its research question: What can children learn about citizenship when they engage with data science? The case study in this paper illustrated ways that children’s learning about citizenship were enriched through an age-appropriate data science investigation. The study analysed classroom video from a Year 4 classroom (aged 9–10) over six lessons that integrated curricula in digital technologies, health, and mathematics. In these lessons, the children generated and analysed non-standard data and debated social, well-being and privacy issues as they considered their activities in cyberspace. The video data were analysed using a framework based on critical citizenship education literature that examined dimensions of power, collective engagement, individual responsibility and action. Three key findings emerged. First, the case study highlighted skills in citizenship education developed through data science, positioning children as agents and advocates. Second, the study showed how a complex data investigation in citizenship education was achievable with primary children through meaningful curriculum integration. This is important given that problems that citizens address are typically interdisciplinary. Finally, the findings revealed a gap between data science skills and those developed in the mathematics curriculum, and recommend ways that the maths curriculum could be updated. Springer Berlin Heidelberg 2023-01-02 /pmc/articles/PMC9806803/ /pubmed/36619684 http://dx.doi.org/10.1007/s11858-022-01450-7 Text en © FIZ Karlsruhe 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Makar, Katie
Fry, Kym
English, Lyn
Primary students’ learning about citizenship through data science
title Primary students’ learning about citizenship through data science
title_full Primary students’ learning about citizenship through data science
title_fullStr Primary students’ learning about citizenship through data science
title_full_unstemmed Primary students’ learning about citizenship through data science
title_short Primary students’ learning about citizenship through data science
title_sort primary students’ learning about citizenship through data science
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9806803/
https://www.ncbi.nlm.nih.gov/pubmed/36619684
http://dx.doi.org/10.1007/s11858-022-01450-7
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