1

Introduction

On September 13 and 14, 2022, the Board on Science Education at the National Academies of Sciences, Engineering, and Medicine held a workshop entitled “Foundations of Data Science for Students in Grades K–12.” Speakers and participants joined the workshop both in-person in Washington, D.C., and virtually via Zoom. Heidi Schweingruber, director of the Board on Science Education, welcomed participants to the workshop and gave a brief overview of previous work conducted by the National Academies related to the topic. Over a decade ago, she said, A Framework for K–12 Science Education (National Research Council, 2012) was published; this report called out the importance of mathematics and data analysis. A National Academies of Sciences, Engineering, and Medicine 2018 report, Data Science for Undergraduates, catalyzed conversations around data science education in K–12. If data science is important in undergraduate school, “why aren’t we attending to what we’re doing in K–12?” asked Schweingruber. Other activities and reports that led to this workshop include STEM Integration in K–12 Education (2014), Cultivating Interest and Competencies in Computing (2021), and the Roundtable on Data Science Postsecondary Education.¹ Work around data science has been accelerating quickly, she said, and this workshop presents an opportunity for stakeholders to discuss different ideas in a collaborative way.

Co-chair Michelle Wilkerson (University of California, Berkeley) highlighted that the workshop was designed to draw attention to the rapidly

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¹https://www.nationalacademies.org/our-work/roundtable-on-data-science-postsecondary-education#:~:text=The%20Roundtable%20on%20Data%20Science,and%20ways%20to%20move%20forward

growing field of K–12 data science education by surveying the current landscape; surfacing what is known; and identifying what is needed to support student learning, develop curriculum and tools, and prepare educators (see Box 1-1). Speakers and participants at the workshop included researchers and practitioners engaged in K–12 data science education from a variety of contexts, including formal and informal; designed and emergent; elementary and secondary; and whose efforts include standalone curricula as well as activities integrated within other content areas (e.g., science, technology, engineering, and mathematics [STEM] disciplines and the humanities). Wilkerson noted that the emphasis of the first day would be on visioning and outcomes whereas the discussions the second day would dig in more concretely to what K–12 data science education looks like in practice. To support these conversations, said Wilkerson, four papers were commissioned and discussed during the workshop:

1. A Secret Agent: K–12 Data Science Learning Through the Lens of Agency²
2. Critical Data Literacy: Creating a More Just World Through Data³
3. Previewing the National Landscape of K–12 Data Science Implementation⁴
4. Tools to Support Data Analysis and Data Science in K–12 Education⁵

This workshop is a “rare and exciting opportunity,” said co-chair Nicholas Horton (Amherst College). Data science in K–12 education is in its infancy, and participants at the workshop have the opportunity to think creatively about how it can best serve students and future citizens. Horton shared four opportunities and challenges that lie ahead for K–12 data science:

1. It is critical that stakeholders act now to ensure that the disparities seen in STEM are not replicated in data science. All students, said Horton, need access to high-quality programs in order to explore and make sense of the data around them.

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²https://www.nationalacademies.org/event/09-13-2022/docs/DD667E469D0EC5DD91A7D85BC839A9852491A3CF9F15

³https://www.nationalacademies.org/event/09-13-2022/docs/D16254F310D01BBDA873920E4EFB8151F2D8334181AA

⁴https://www.nationalacademies.org/event/09-13-2022/docs/D688ED916E82498DA0E2171A109936D679FD5DE26556

⁵https://www.nationalacademies.org/documents/embed/link/LF2255DA3DD1C41C0A42D3BEF0989ACAECE3053A6A9B/file/DB48F8A34F71C395C3071BABFFD42AFFF06478824419

2. There are a plethora of powerful tools and technologies available; however, it is an open question what role these should play in teaching, learning, and lessening disparities. For example, he said, cloud computing and open-source tools have the potential to improve access and lessen disparities, but this will only happen if the technologies are deployed appropriately.
3. There is a need to think about which educational pathways are most promising, and how general knowledge of student learning and development can inform practice in this area.

4. The implementation of effective K–12 data science education will require confident, prepared teachers who have a foundation in data acumen.⁶ The COVID-19 pandemic exacerbated challenges in teacher recruitment, training, and retention, and it is critical that these challenges be addressed.

Horton then offered a list of guiding questions for workshop participants to keep in mind during the workshop presentations and discussions (see Box 1-2).

Horton noted that although these questions were not likely to be answered in full over the two days, he hoped that the workshop would shed light on possible answers, spur fruitful discussions, and connect stakeholders and different communities.

REPORT ORGANIZATION

This proceedings summarizes the presentations and discussions highlighted during the workshop. Proceedings may not contain any consensus analysis or views of the committee on the underlying subject matter of the workshop. This proceedings may contain particular viewpoints attributed to individual participants or groups of participants in the workshop, if

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⁶ “Data acumen” is a phrase used to describe some of the specific skills needed for data science. There are a few different meanings associated with this term but no agreed upon working definition in K–12.

these attributions can be adequately documented and if the viewpoints are reasonable statements for inclusion in a National Academies proceedings. Chapter 2 explores the vision for data science education in K–12: What is it that we want students to be able to do with data, and how can we assess and measure the intended outcomes? Chapter 3 looks at the current landscape of data science education, and features speakers working on the ground and in the classroom. Chapter 4 discusses the tools, resources, and teacher preparation that are needed to implement the vision of data science education, and Chapter 5 looks toward the future by identifying priorities for research and funding.

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