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What do we know about data science learning at the grades K–12 (precollegiate) 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. 

We present an interview study of 6th grade math and science teachers’ expressed goals for engaging their students with data. We explored this across disciplinary boundaries to contribute to a body of knowledge that can support the development of a more coherent experience for students across math and science classes. Our teachers were all highly motivated to engage their students with data, and all wanted their students to see things with their data models. However, we observed consequential differences in the kinds of things they wanted students to see. Here we describe these differences and discuss potential implications for practice.