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2 Vision for K12 Data Science Education and Outcomes
Pages 7-22

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From page 7...
... CREATING A VISION OF "HIGH-QUALITY" K–12 DATA SCIENCE EDUCATION In this session of the workshop, speakers and participants explored what defines a valuable learning experience for students, what research has revealed about successful and unsuccessful curricular interventions, and how these learnings can be articulated into policy and practice.
From page 8...
... The onesize-fits-all approach to math education has sent a message to these students that they do not belong and that they cannot be a "math person." To address this issue and to reach all students with data literacy efforts, he said, it is essential that math education be presented in a relevant and useful way. For example, if students can see explicit links between math education and the world around them, they may be encouraged to see themselves as "math people" and pursue further education in the area.
From page 9...
... Who ends up participating in data science, and what will data science look like in the future? Spector noted the "dual nature" of data science education.
From page 10...
... "Data science should be integrated everywhere," said Shelton. Two of the key elements in science education are relevance and authenticity, she said; studying complex and relevant phenomena helps to keep students engaged and interested.
From page 11...
... For example, K–12 educators are unlikely to have the time necessary to sit down and collaborate and develop data science curriculum; mitigating this challenge may require providing teachers with more time or engaging more stakeholders in the process. Shelton added that professional learning will be a critical part of implementing data science education in K–12; research has found that many science teachers are hesitant and uncomfortable with the topic.
From page 12...
... Teaching the skills before the understanding, he said, is "putting the cart before the horse." Gould noted that he prefers the term "data acumen" rather than "data literacy." Spector added that while data science has become its own discipline, it is built upon multiple other disciplines. Although it is important to teach a holistic and unified view of data science, it is also critical to provide opportunities for students to gain an in-depth understanding of the underlying disciplines (e.g., statistics, computation)
From page 13...
... Many different communities are generating and revising knowledge using data science ideas and practices, and technologies and methods change quickly. Second, research on data science learning is happening in diverse contexts, including K–12 classrooms and undergraduate classrooms, and across diverse theoretical traditions.
From page 14...
... Second, these studies focus on the ways in which students can be positioned to see data as personally meaningful, can construct coherent data stories, and can develop data science approaches that resemble disciplinary approaches in meaningful ways. Jones noted that personal agency is not a "magic bullet." It takes a lot of work to design environments and to scaffold and sequence learning so that learners can exert personal agency and also make progress toward generating answers to the questions on which they are working.
From page 15...
... Critical data literacy expands on data literacy to include an understanding of when and how data are collected from us, an understanding of algorithms and what they do, and a consideration of the ethical impacts of data collection and data-based decisions. Further, critical data literacy requires awareness of unequal power structures in society and how data can be used to perpetuate or worsen social inequality.5 Louie said that "there is a tradition in educational research and practice that looks critically at unjust structures in society and the role that education and data may play in questioning and countering these structures to create a more just world." Applying these ideas, said Louie, critical data literacy can be seen as "learning to read and write the world with data." Reading the world 4For more information about the methods and resources described throughout the presen tation, see https://www.nationalacademies.org/event/09-13-2022/docs/D16254F310D01BBD A873920E4EFB8151F2D8334181AA 5Books such as Data Feminism, Weapons of Math Destruction, and Algorithms of Op pression make these arguments.
From page 16...
... Part of this work, said Louie, is using data to help non-dominant groups build pride in their own cultural and social identities. Louie described some takeaways from a commissioned paper she wrote for the workshop.6 In the paper, she highlighted eight examples of interventions aimed at promoting critical data literacy in K–12 education.
From page 17...
... First, most studies did not report a strong focus on quantitative reasoning. If critical data literacy can be separated from quantitative reasoning, she said, we need to consider how it differs from critical information literacy or critical media literacy.
From page 18...
... Boaler concurred that the presentations covered topics that are essential aspects of data science. A framework of agency highlights the importance of students asking questions, building models, interpreting data, and communicating, whereas critical data literacy focuses on students' ability to understand and shape their world using data.
From page 19...
... Horton commented that this idea points to the need for collaboration and coordination of data science curricula across multiple levels of education. Relationship to Other Education Movements A virtual workshop participant commented that if you look at computer science standards, the end goal is often working with real-world data; she asked whether this goal is consistent with the goals of the data science education movement.
From page 20...
... shared some information and resources about Indigenous data sovereignty. She said that this topic is critical for those working in data science education and encouraged workshop participants to read works by scholars including Stephanie Russo Carroll, Desi Rodriguez Lonebear, Lynn Lavalley, Eve Tuck, and Linda Tuhiwai Smith.
From page 21...
... Jones agreed and said that citizen science can look very different from place to place, and it is important to pay attention to what types of agency are being prioritized. For example, a project in which students are measuring water pollutants may use methods given to them by a local researcher.


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