Curating datasets to support middle school student inquiry
DOI:
https://doi.org/10.52041/iase2023.106Abstract
We examine how developers of data science curricula determine what makes a pedagogically effective dataset enabling 10–14 year-old students (“middle school” in the United States) to engage in the data investigation cycle by posing their own questions about relationships among variables. We describe strategies for curating existing datasets to address goals for learning about data, and for optimizing the use of these datasets once they are curated. We investigate how data science educators can transform existing datasets into ones appropriate for students with little data experience, drawing on our experience working with several publicly available datasets, which students explored in CODAP (the Common Online Data Analysis Platform) (Concord Consortium, n.d.).References
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