Teach Data Literacy: A Guide for Primary Teachers

Judy Robertson; Holly Linklater; Kate Farrell; Jasmeen Kanwal; Serdar Abaci; Claire Sowton; Elspeth Maxwell

doi:10.2218/ED.9781836451396

Teach Data Literacy: A Guide for Primary Teachers

Authors

Judy Robertson, University of Edinburgh; Holly Linklater, University of Edinburgh; Kate Farrell, University of Edinburgh; Jasmeen Kanwal, University of Edinburgh; Serdar Abaci, University of Edinburgh; Claire Sowton, University of Edinburgh; Elspeth Maxwell, Dog & Fox Design

DOI: https://doi.org/10.2218/ED.9781836451396

Keywords:

Data Literacy, Teaching Data in Schools, Primary Teachers, Data Problem Solving

Synopsis

Do you wince at the thought of data literacy, or sigh with relief that it is it now recognised as an essential part of all children and young people’s learning? Either way, this handbook is here to help.

Data literacy is the set of skills and concepts which people need to understand, interpret and make decisions based on the data they encounter in the world around them. There is no official area of the

curriculum, or topic, labelled “data literacy” in the Curriculum for Excellence in Scotland. However it is relevant across the curriculum in outcomes for maths, literacy, technologies, and social studies.

This handbook sets out how, building on children and young people’s curiosity about their world, teachers can enhance opportunities for all to build the skills and habits of mind relevant to data problem-solving.

Introduces and explains what is meant by ‘data literacy’ and how it relates to the school curriculum, including key terms and concepts
Provides a clear framework for thinking about data problem-solving
Reviews a wide range of resources for early, first and second level that can be used to support teaching and learning about data literacy

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References

OECD. (2019). Well-being 2030 Action OECD Future of Education and Skills 2030: A Series of Concept Notes. www.oecd.org.

Wolff, A., Gooch, D., Cavero Montaner, J.J, Rashid, U., Kortuem, G., (2016). Creating an Understanding of Data Literacy for a Data-driven Society. The Journal of Community Informatics, 12(3), 9-26. P23

There are some great examples of job opportunities in data on our free poster at: https://dataschools.education/put-your-data-skills-to-work/

Wild, C. J. (2017). Statistical Literacy as the Earth Moves. Statistics Education Research Journal, 16(1), 31–37. P32

This quote is from a letter from Einstein to Carl Seelig, March 11, 1952, AEA 39-013: https://www.asl-associates.com/einsteinquotes.htm

You can find a complete list of data literacy related outcomes and expectations across the curriculum here: https://dataschools.education/resource/experiences-and-outcomes/

Spiegelhalter, D. J. The Art of Statistics : Learning from Data / David Spiegelhalter. UK: Pelican, an imprint of Penguin Books, 2019. Print. P18

Wild, C. J. (2017). Statistical Literacy as the Earth Moves. Statistics Education Research Journal, 16(1), 31–37. P32

Note that this is a slightly different meaning to “variable” in algebra or computer programming.

Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. A. (2020). Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) A Framework for Statistics and Data Science Education. https://www.amstat.org/education/guidelines-for-assessment-and-instruction-in-statistics-education-(gaise)-reports

Adapted from Wolff, A., Gooch, D., Montaner, C., Rashid, J. J., Kortuem, U., Wolff, A., Kortuem, G. (2016). Creating an Understanding of Data Literacy for a Data-driven Society. The Journal of Community Informatics, 12(3), 9–26. Retrieved from www.ci-journal.net/index.php/ciej/article/view/1286.

You can find some examples of real-world datasets for classroom use here: https://dataschools.education/resource/seasonal-datasets/ and https://concord-consortium.github.io/codap-data/

https://5rightsfoundation.com/KnowYourRightsPoster.pdf

Harford, Tim. How to Make the World Add up / Tim Harford. The Bridge Street Press. 2020. Print. P294

Spiegelhalter, D. J. The Art of Statistics : Learning from Data / David Spiegelhalter. UK: Pelican, an imprint of Penguin Books, 2019. Print. P15

https://www.co2indicator.nl/documentatie/Ventilation-Rates-in-Schools-and-Pupils-Performance.pdf

http://www.iaquk.org.uk/ResourcesCO2.html

https://www.gapminder.org/tools/

https://codap.concord.org/

Harford, Tim. How to Make the World Add up / Tim Harford. The Bridge Street Press. 2020. Print. P10

Silver, Nate. The Signal and the Noise : the Art and Science of Prediction / Nate Silver. London: Penguin Books, 2013. Print. P9

Harford, Tim. How to Make the World Add up / Tim Harford. The Bridge Street Press. 2020. Print. P280

Available from: https://dataschools.education/data-education-resources/

Arnold, P., & Franklin, C. (2021). What Makes a Good Statistical Question? Journal of Statistics and Data Science Education, 29(1), 122–130. https://doi.org/10.1080/26939169.2021.1877582