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Foundations of Data Science for Students in Grades K-12 Proceedings of a Workshop (2023) / Chapter Skim
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5 Looking Forward
Pages 67-76

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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...
... Carol Bertram, a high school teacher in Chicago, offered her request that more work be done to connect math and statistics to social awareness and social justice. Bertram said that she gets "a lot of leeway" in the data science class she teaches, but she currently is on her own to find data, develop questions, and figure out how to engage students.
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From page 69...
... For example, integrating computer science into preservice teacher methods classes has been a struggle; the field of data science can learn from these experiences and lessons. The work that needs to be done, said Colleen McEnearney (Tuva)
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From page 70...
... Townhall Day 2: How Can Practice Inform Current and Future Research Needs? At the end of the second day, Horton asked workshop participants to think holistically, broadly, and in a "visionary fashion" about how teaching practice can inform current and future research needs in the area of data science education based on the discussions throughout the day, and then opened the floor for conversation (see Box 5-2 for a summary of participant-identified needs that are informed by teaching practice)
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From page 71...
... . Changes need to be made at multiple levels, including changes to state standards for licensure, district-level changes to curriculum, shifts in how high school counselors and college admissions officers think about data science, and increases in the accessibility of data tools.
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From page 72...
... Second, the "promise and perils" of data mean that we must start developing data literacy skills for all students, starting with the earliest learners, using a student-centered approach that spans all of K–12. This may include standalone data science courses, data science that is integrated into other courses, or both.
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From page 73...
... The California Education Learning Lab, said Lark Park (California Education Learning Lab) , is a state-funded higher education grant program that funds faculty doing innovative work to improve learning outcomes and close equity gaps at the three large public systems.
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From page 74...
... " There are several overlapping and discrete goals, including training students to be data scientists and helping students become data-literate members of society. The broadness of data science requires developing a broad view of what counts as expertise and pulling from a diverse pool of expertise to support learners and teachers.
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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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From page 76...
... We do not want to repeat the mistakes of the past, so we must reverse the trend for the growing divide to make and keep data science broad. Diversity will bring a lot of ideas and voices to the table, which may lead to significantly fewer models producing biased results when trained using algorithms on biased data sets.
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Key Terms

  • science education
  • data literacy
  • learning lab
  • teaching data
  • teach data

  • integrate data
  • science courses
  • education learning
  • improve learning
  • learning outcomes

  • learning objectives
  • california education
  • professional learning
  • discussions throughout
  • planning committee

  • schmidt futures
  • social justice
  • inform current
  • teaching practice
  • learning data

  • critical data
  • data divide
  • data tools
  • data fluency
  • biased data

  • developing data
  • adding data
  • defining data
  • integrating data
  • data scientists


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