Foundations of Data Science for Students in Grades K-12 Proceedings of a Workshop (2023) / Chapter Skim
Currently Skimming:
Pages 16-36
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 16... ...
Part of this work, said Louie, is using data to help non-dominant groups build pride in their own cultural and social identities. Louie described some takeaways from a commissioned paper she wrote for the workshop.6 In the paper, she highlighted eight examples of interventions aimed at promoting critical data literacy in K–12 education.
|
From page 17... ...
These discussions can be particularly harmful to students when teachers are not trained in how to conduct them well, she said. In conclusion, Louie said that learning to read and write the world with data may be a helpful framework for promoting critical data literacy because reading and writing with text are already fundamental and familiar goals in K–12 education.
|
From page 18... ...
To set the stage for discussion, workshop participants contributed their answers to the question: "What are some desired outcomes for K–12 data science education? " A word cloud was made with their answers (Figure 2-2)
|
From page 19... ...
Horton commented that this idea points to the need for collaboration and coordination of data science curricula across multiple levels of education. Relationship to Other Education Movements A virtual workshop participant commented that if you look at computer science standards, the end goal is often working with real-world data; she asked whether this goal is consistent with the goals of the data science education movement.
|
From page 20... ...
shared some information and resources about Indigenous data sovereignty. She said that this topic is critical for those working in data science education and encouraged workshop participants to read works by scholars including Stephanie Russo Carroll, Desi Rodriguez Lonebear, Lynn Lavalley, Eve Tuck, and Linda Tuhiwai Smith.
|
From page 21... ...
Jones agreed and said that citizen science can look very different from place to place, and it is important to pay attention to what types of agency are being prioritized. For example, a project in which students are measuring water pollutants may use methods given to them by a local researcher.
|
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.
|
From page 24... ...
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.
|
From page 25... ...
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.
|
From page 26... ...
Many teachers did not see the connection between data science and the subject of the class, so there was a lot of attrition between summertime professional development and actual implementation. The second lesson was that a six-week module is not enough time to teach the concepts deeply, or for students to get to interact with data as much as they should have.
|
From page 27... ...
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.
|
From page 28... ...
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.
|
From page 29... ...
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.
|
From page 30... ...
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.
|
From page 31... ...
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.
|
From page 32... ...
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.
|
From page 33... ...
The goal of this session is to explore the research on the settings and contexts of K–12 data science education with an emphasis on what data science looks like in these contexts and the connections with broader informal contexts that are relevant to K–12 learners' lives. Each panelist in the session made brief remarks about their work, followed by a Q&A session moderated by Clegg.
|
From page 34... ...
. 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/
|
From page 35... ...
. Santo emphasized that these theoretical tensions need to be taken seriously when considering whether and how to integrate data science into other subject areas.
|
From page 36... ...
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.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.