Foundations of Data Science for Students in Grades K-12 Proceedings of a Workshop (2023) / Chapter Skim
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3 Current Landscape of Data Science Education
3 Current Landscape of Data Science Education
Pages 23-50
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From page 23... ...
HEARING FROM PRACTICE This session explored the reality on the ground in data science education, with a deep focus on the specifics of designing student learning opportunities. Topics included student learning progressions, opportunities for integration between data science and other subjects, and the wraparound resources needed for implementation.
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In a paper commissioned for the workshop, Drozda and his colleagues used a variety of methods to survey the landscape of K–12 data science implementation, but he cautioned that the data are incomplete.1 Drozda shared a map (Figure 3-1) of the states that had statewide discrete data science education programs as of the summer of 2022; he noted that this map excludes hundreds of individual school and district programs.
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However, Drozda cautioned that these existing guidelines are potentially not enough for data science education. He and his colleagues also conducted a series of stakeholder interviews across the country to surface the perspectives of teachers.
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were awarded a grant to bring statistical and computational thinking to math, science, and computer science classrooms, said Suyen Machado (UCLA)
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The plan is to expand data science education into all courses, from pre-K through grade 12, said Melville. However, there are a lot of details to figure out.
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said that her work with data science programs could be best described as "first doing the work and then naming it much later." She explained that 12 years ago, when her work involved supporting young people to collect data about the lack of transportation in their cities, data science was not a construct that she was aware of. However, the process of collecting data and using those data to make arguments for resources in the community brought up many of the same issues and questions that have been discussed at this workshop, she said.
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Moderated Discussion and Audience Q&A Following the panelists' remarks, Drozda moderated a Q&A session followed by audience Q&A. Program Design Drozda asked the panelists to comment on how educators could think about designing their own programs, and to describe what an "exciting, meaningful, and impactful" K–12 data science education experience looks like.
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Rather than starting with the data or the calculations, she said she has found the most success when she allows students to "look out into the world" to see what relationships might exist and how they might detect them. Melville said that while practicing calculations is important, it doesn't always bring more students to the table of conceptual understanding; her primary goal is ensuring that data science education is meaningful, impactful, and powerful, particularly for historically excluded populations.
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She said implementing data science education will require changes in the messaging and actions in higher education. Taylor encouraged workshop participants to think about how to distribute the onus of teaching across a variety of professionals with disciplinary expertise in making sense of data.
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observed that many of the activities discussed at the workshop are already happening in science classrooms (e.g., videos of data visualizations from the National Aeronautics and Space Administration) , and wondered whether there are conversations being held about how to strengthen data science education within science courses.
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CONTEXTUAL FACTORS FOR K–12 DATA SCIENCE EDUCATION Previous speakers have noted that when learning is relevant to youth, they engage more deeply in it and begin to initiate and drive their own learning, said Tammy Clegg (University of Maryland)
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. Santo shared his insights into the institutional dynamics related to bringing data science into K–12 settings, based on his experience with the Integrated Computational Thinking project.7 Through his work on computer science education, Santo and his colleagues found that K–12 administrators were looking to develop K–12 groups and sequences 6https://wp.nyu.edu/riddle/projects/building-data-literacy-through-the-arts/ 7https://ctintegration.org/
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Santo highlighted one integration pathway: analyzing texts through computational methods; engaging in data practices for social studies inquiry; and seeing data in art and making data as art. Santo noted that in the course of their research, he and his colleagues identified a number of tensions that speak to the epistemic and cultural differences between computational thinking and the humanities: contextual reductionism, procedural reductionism, epistemic chauvinism, threats to epistemic identities, and epistemic convergence.8 He highlighted the tension of "epistemic chauvinism," in which computational thinking epistemologies are elevated at the expense of the epistemologies of other subject areas; this can lead to the sidelining of existing ways of knowing, he said.
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Another project brought together educators, designers, learning scientists, and tool developers to identify barriers and opportunities for equitable data science education at the high school level. This convening resulted in several conclusions, said Uzzo, including the need for inclusive tools, resources, and curricula; the need for teacher support and teacher enfranchisement in the process; the need for integrating data science across the curriculum; and the need to make data science available to all students.
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Working with Qualitative and Quantitative Data Clegg began by asking panelists to comment on how they see students understanding and working with quantitative and qualitative data, and if there are ways that data science education needs to shift to better convey the rich context of data. Uzzo responded that using quantitative and qualitative data together to better understand issues such as climate change is an emerging area that educators need to focus on more in order to give students and community members a better understanding of what data are and how they can be used.
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He noted that in his work, they start with the assumption that the teachers don't care about computational thinking or data science. "Then it's our job to figure out" how a data science approach can enhance the work of these teachers.
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Overcoming Math Phobia A virtual workshop participant noted that "math phobia" is common, even among teachers, and asked how to overcome this barrier to incorporating data science. DesPortes responded that in her work with math and art teachers, each side was "phobic" of the other side's discipline.
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Data science education can and should draw on diverse datasets, such as climate data, data on COVID-19, and data on gerrymandering. Schanzer said that people who are "scratching their heads" trying to figure out how to integrate data science with non-STEM classes are focusing too heavily on the first two ingredients of data science education (statistics and computing)
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That won't help everyone." • "It's never only about COVID-19." Calabrese Barton explained that viewing the pandemic as a socioscientific issue makes visible how lives are rendered through data, quantifying and categorizing people in communities. In data science education, Calabrese Barton draws upon a data justice framework to explore how and why youth engage with data to make sense of, to make decisions about, and to take action on science-related issues.
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Calabrese Barton described this process as Prez "producing critical data practices overlaying YouTube with the big data to critique power structures that determine what data and data narratives count, to care for himself when data, society, and science [were] not." As a Black youth, Prez encountered dominant data narratives that invoked harmful racialization and he used critical data practices to move beyond critique toward liberatory outcomes.
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Radinsky said that data science education should reflect this approach of using data to construct meaning for our everyday lives. His third area of work involves using data to advocate for educational justice in Chicago.
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. Critical data expression, which is at the intersection of technology, justice, and art, involves using data as storytelling material, and discovering, analyzing, and sharing data in expansive ways that engage young people and people in positions of power to bring change.
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. Moderated Discussion and Audience Q&A Following the panelists' remarks, Matuk led a Q&A session with panelists and workshop participants.
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She asked panelists to comment on what lessons they have learned through their work about how to best approach socially and politically sensitive issues through a data lens. Schanzer responded that at the same time that he and his colleagues were working in New York City to implement data science education, there was a separate effort to help teachers engage in difficult conversations with students.
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She shared the example of a story on AI tools that were being used to reanimate still photographs of people who had died; as the reporter was investigating, the story turned away from the details of the technology and more toward an exploration of grief and societal support for those who are grieving. Applying Lessons to K–12 Classrooms Given the broad range of experiences discussed in this session, Matuk asked panelists to consider what they had learned from their work that could be applied to data science education in the K–12 system.
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Bhargava shared a story about a fellow teacher's experience with teaching data science in a high school math class. Students were given the assignment of collecting and producing data about an issue they were passionate about, and then giving a presentation on their findings.
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She asked panelists how these types of data science activities could be scaled for implementation into classrooms. Bhargava began by noting that the Data Culture Project14 website has a suite of 12 activities with curricular guides for teachers; the activities are small and easy to weave into the work of the classroom.
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