Foundations of Data Science for Students in Grades K-12 Proceedings of a Workshop (2023) / Chapter Skim
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Pages 37-48
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From page 37... ...
She asked panelists to comment on how to get buy-in from stakeholders, and how engagement with data science can be done in a way that allows stakeholders to do it "on their own terms and in their own ways." Chetty responded that "buy-in is really tough." Educators have an enormous amount on their plate already and bringing in data science adds to their load. Data science learning needs to be everywhere -- from math class to art class -- but getting buy-in from teachers will require professional development and resources to give them the confidence and the tools they need to integrate data science into their classes.
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From page 38... ...
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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From page 39... ...
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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From page 40... ...
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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From page 41... ...
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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From page 45... ...
. 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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From page 46... ...
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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From page 47... ...
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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From page 48... ...
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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