Foundations of Data Science for Students in Grades K-12 Proceedings of a Workshop (2023) / Chapter Skim
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Pages 63-75
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From page 63... ...
Lee asked panelists to describe the existing research on teacher preparation for data science education and to identify areas where there are research gaps. One thing we know about teacher learning, said Leftwich, is that teachers want to find ways to make an impact on their students and their learning.
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From page 64... ...
Rosenberg agreed that clarity is needed on what data science is; for example, teachers may want to do more than data visualization but aren't sure what the next step is. Leftwich said that in order to move data science forward, there is a need for more readily accessible and free software that allows students and teachers to use and manipulate data easily.
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From page 65... ...
Shifting Course Requirements Given the discussion earlier in the session about how requirements for teacher education largely drive the curriculum, Melville asked panelists if and how these requirements might be changed to require courses in data science education. "Is that something worth making a noise about," she asked, or is it best to continue on the path to infusing it into existing courses?
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From page 66... ...
Lee, that can be creatively interpreted by the university. Rosenberg added that he would like to learn from the computing education world how it has addressed the issue of state requirements to see if lessons could be applied to data science education.
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From page 67... ...
Wilkerson began the townhall session by asking workshop participants to reflect on the discussions throughout the day at the workshop, and to use these as a jumping-off point for identifying priorities for additional research in the area of data science education (see Box 5-1 for a summary of participant-identified priorities)
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From page 68... ...
• Applications of critical data literacy in relation to student and teacher per ceptions of mathematical identities and transference of mathematics to their communities (virtual participant) • Viable ways to have common underlying habits of mind and processes that may just look different from one tool to another (in the use of tools that are currently geared toward teaching, learning, data literacy, and data science)
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From page 69... ...
, is not in convincing teachers to buy in to data science education but in persuading districts and policymakers of its importance. McEnearney said she would have loved to use data science tools and techniques in her years as a high school math teacher, but she felt constrained by all of the requirements and testing that had to happen.
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From page 70... ...
began the session by asking to broaden the perspective beyond practice to include experiences, products, and perspectives. Leinwand described himself as a former "hippie math teacher" and a "self-proclaimed mathematics education change agent." Based on his experiences through the years, Leinwand emphasized BOX 5-2 List of Participant-identified Needs Informed by Teaching Practice Discussions • Clear conceptualization of data science education that is created by a di verse group of stakeholders (Leinwand)
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From page 71... ...
There is a need for a curriculum-agnostic and tool-agnostic assessment of what students are learning when they are learning data science. With this knowledge, he said, we could make a better argument to universities about the value of data science education and how it prepares students for higher education and the workforce.
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From page 72... ...
Lue said that this is an "exciting juncture" for the field of data science education and invited two other funders to make remarks. Ulrich Boser (Schmidt Futures)
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From page 73... ...
After offering general grants for STEM, as well as specific grants for calculus reform, the Learning Lab became interested in creating an opportunity for data science because of its importance to other disciplines, the workforce, and productive citizens of the world. The Learning Lab announced a Grand Challenge in Building Critical Mass for Data Science2 to help create an inflection point in data science education.
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From page 74... ...
When designing a tool for educational use, the student and teacher experience with using the tool should be kept front-and-center. Clegg asked, "What is the end goal of data science education?
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From page 75... ...
In addition, data science is already naturally integrated in the professional work of many fields, including biology, social science, and journalism. Instead of trying to find places for data science in classes, Matuk suggested that stakeholders consider how to make learning experiences "more authentic to those domains in and of themselves." That is, instead of teaching data science for its own sake, she said that educators should integrate data science because it is an integral part of the domain that is being taught.
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