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Interdisciplinary Data Education: Teaching Primary and Secondary learners how to be data citizens

Authors:

Kate Farrell,

Judy RobertsonAuthors Info & Claims

WiPSCE '19: Proceedings of the 14th Workshop in Primary and Secondary Computing Education

Article No.: 17, Pages 1 - 2

https://doi.org/10.1145/3361721.3362120

Published: 23 October 2019 Publication History

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Abstract

As the use of data science continues to permeate business and civic life, school systems must adapt to prepare young people not only for employment in data related roles, but also to be active citizens within a data society. This workshop introduces the Data Education in Schools project which is designed to educate 3-18 year old learners about data across the curriculum. We will present an interdisciplinary curriculum framework for data education, based on the PPDAC cycle for statistical literacy and adapted for the Scottish Curriculum for Excellence. We will illustrate how values-based authentic problem solving projects related to real world data can integrate curriculum outcomes from computer science, mathematics and the social subjects. We will explain the rationale for the new Data Science qualification for school learners which is now offered by the Scottish Qualifications Authority. Workshop attendees be invited to share information and experiences about data education from their own educational settings and act as critical friends to improve the Data Education in Schools project.

References

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Bhargava, R. et al. 2015. Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data (Data-Pop alliance white paper series).

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Burning Glass Technologies 2017. The Quant Crunch: how the demand for data science skills is disrupting the job market.

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High Workload Leaves 75% of Teachers Frequently Feeling Stressed: 2019. https://www.eis.org.uk/Latest-News/Stress. Accessed: 2019-09-20.

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Tang, R. and Sae-Lim, W. 2016. Data science programs in U.S. higher education: An exploratory content analysis of program description, curriculum structure, and course focus. Education for Information. 32, 3 (2016), 269--290.

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Wolff, A. et al. 2016. Creating an Understanding of Data Literacy for a Data-driven Society. The Journal of Community Informatics. 12, 3 (2016), 9--26.

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Cited By

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Jiang WZhang Y(2024)Forging synergiesJournal of Computational Methods in Sciences and Engineering10.3233/JCM-24747324:4-5(2485-2501)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JCM-247473
Farrell KWatts JRobertson JLawson T(2023)A Flexible School and College Level Qualification in Data ScienceProceedings of the 18th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3605468.3609769(1-2)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3605468.3609769
Johnson BRydal Shapiro BDiSalvo BRothschild ADiSalvo CKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Exploring Approaches to Data Literacy Through a Critical Race Theory PerspectiveProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445141(1-15)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445141

Index Terms

Interdisciplinary Data Education: Teaching Primary and Secondary learners how to be data citizens
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. K-12 education
      2. Model curricula

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Published In

WiPSCE '19: Proceedings of the 14th Workshop in Primary and Secondary Computing Education

October 2019

127 pages

ISBN:9781450377041

DOI:10.1145/3361721

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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GI: Gesellschaft für Informatik e.V.
University of Glasgow: University of Glasgow

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Association for Computing Machinery

New York, NY, United States

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Published: 23 October 2019

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University of Edinburgh Data Driven Innovation

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WiPSCE'19

WiPSCE'19: 14th Workshop in Primary and Secondary Computing Education

October 23 - 25, 2019

Scotland, Glasgow, Uk

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WiPSCE '19 Paper Acceptance Rate 23 of 43 submissions, 53%;

Overall Acceptance Rate 104 of 279 submissions, 37%

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Cited By

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Jiang WZhang Y(2024)Forging synergiesJournal of Computational Methods in Sciences and Engineering10.3233/JCM-24747324:4-5(2485-2501)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JCM-247473
Farrell KWatts JRobertson JLawson T(2023)A Flexible School and College Level Qualification in Data ScienceProceedings of the 18th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3605468.3609769(1-2)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3605468.3609769
Johnson BRydal Shapiro BDiSalvo BRothschild ADiSalvo CKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Exploring Approaches to Data Literacy Through a Critical Race Theory PerspectiveProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445141(1-15)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445141
Saddiqa MMagnussen RLarsen BPedersen J(2021)Open Data Interface (ODI) for secondary school educationComputers & Education10.1016/j.compedu.2021.104294174:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.compedu.2021.104294

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Seeing the Whole Elephant - A Comprehensive Framework for Data Education

Computer Science Education in North-Rhine Westphalia, Germany—A Case Study

Sharing computer science education ideas: Israeli-Russian collaboration

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Seeing the Whole Elephant - A Comprehensive Framework for Data Education

Computer Science Education in North-Rhine Westphalia, Germany—A Case Study

Sharing computer science education ideas: Israeli-Russian collaboration

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