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Data Science Learning in Grades K–12: Synthesizing Research Across Divides

Forthcoming. Now Available: Just Accepted Version.
Published onJun 06, 2024
Data Science Learning in Grades K–12: Synthesizing Research Across Divides
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Abstract

What do we know about data science learning at the grades K-12 (pre-collegiate) level? This article answers this question by using the notion of agency to provide a framework to review the diverse research agendas and learning environments relevant to data science education. Examining research on data science education published in three recent special issues, we highlight key findings from scholars working in different communities using this lens. Then, we present the results of a co-citation coupling analysis for articles published in one of three recent data science education special issues with research spanning various levels and contexts. This co-citation analysis showed that while there are some common touchpoints, research on data science learning is taking place in a siloed manner. Based on our review of the literature through the lens of agency and our analysis, we discuss how the data science education community can synthesize research across disciplinary and grade-level divides.

Keywords: data science education, pre-collegiate, grades K-12, literature review, co-citation coupling, statistics education



06/06/2024: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.


©2024 Joshua Rosenberg and Ryan Seth Jones. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.

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