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From page 5... ... 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. Read the entire page →
From page 7... ... Moreover, to ensure that all students have access to high-quality programs and are able to make sense of the data around them, sessions were designed around the following questions: Why are we interested in data science at the K–12 level? What outcomes do we want for students, for the practice of data science education, etc.? Read the entire page →
From page 8... ... Recio said that data science presents an opportunity to make these types of linkages for students, and that using data science to make math more relevant makes it possible to change the message about "who belongs in math courses and who can be successful." There are three overwhelmingly important reasons to address data science in K–12 education, said Alfred Spector (Massachusetts Institute of Technology) Read the entire page →
From page 9... ... Identifying "Success" for K–12 Data Science Education Given these reasons for why data science education is important, how can we be sure we are doing it well? What does it mean to be successfully educating students in terms of what they learn? Read the entire page →
From page 10... ... Looking at data science can be like looking at an elephant, he said; people from different disciplines and industries may all see a different part of the elephant. When implementing data science education into K–12, there is a need to focus on the essential ideas and approaches that apply to all disciplines. Read the entire page →
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. Read the entire page →
From page 12... ... There is a false dichotomy between teaching data literacy and data science, said Gould. Before teaching data science -- and specific tools such as machine learning -- students need a foundational understanding in data literacy. Read the entire page →
From page 13... ... He added, however, that it is important to prepare teachers to be able to confidently work with available technologies and tools, and that they need continuous learning and support in this area. Gould said that the "elephant in the room" is the cost of technologies; obtaining and updating technology for the classroom is "extraordinarily expensive." A VISION FOR K–12 DATA SCIENCE LEARNING AND OUTCOMES This session of the workshop was designed to explore evidence on learning and critical data literacy to consider what students should be able to do with data and how outcomes should be measured. Read the entire page →
From page 14... ... Examining these different types of agency, said Jones, led to several questions and considerations about how to move forward with the work of K–12 data science education: • Should different forms of agency be prioritized depending on where the work is oriented? • What is the role of systems-level organizations and support to synthesize the work? Read the entire page →
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. Read the entire page →

From page 5...

... 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.

Read the entire page →

From page 7...

... Moreover, to ensure that all students have access to high-quality programs and are able to make sense of the data around them, sessions were designed around the following questions: Why are we interested in data science at the K–12 level? What outcomes do we want for students, for the practice of data science education, etc.?

Read the entire page →

From page 8...

... Recio said that data science presents an opportunity to make these types of linkages for students, and that using data science to make math more relevant makes it possible to change the message about "who belongs in math courses and who can be successful." There are three overwhelmingly important reasons to address data science in K–12 education, said Alfred Spector (Massachusetts Institute of Technology)

Read the entire page →

From page 9...

... Identifying "Success" for K–12 Data Science Education Given these reasons for why data science education is important, how can we be sure we are doing it well? What does it mean to be successfully educating students in terms of what they learn?

Read the entire page →

From page 10...

... Looking at data science can be like looking at an elephant, he said; people from different disciplines and industries may all see a different part of the elephant. When implementing data science education into K–12, there is a need to focus on the essential ideas and approaches that apply to all disciplines.

Read the entire page →

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.

Read the entire page →

From page 12...

... There is a false dichotomy between teaching data literacy and data science, said Gould. Before teaching data science -- and specific tools such as machine learning -- students need a foundational understanding in data literacy.

Read the entire page →

From page 13...

... He added, however, that it is important to prepare teachers to be able to confidently work with available technologies and tools, and that they need continuous learning and support in this area. Gould said that the "elephant in the room" is the cost of technologies; obtaining and updating technology for the classroom is "extraordinarily expensive." A VISION FOR K–12 DATA SCIENCE LEARNING AND OUTCOMES This session of the workshop was designed to explore evidence on learning and critical data literacy to consider what students should be able to do with data and how outcomes should be measured.

Read the entire page →

From page 14...

... Examining these different types of agency, said Jones, led to several questions and considerations about how to move forward with the work of K–12 data science education: • Should different forms of agency be prioritized depending on where the work is oriented? • What is the role of systems-level organizations and support to synthesize the work?

Read the entire page →

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.

Read the entire page →

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