| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 23, 2017 |
| Latest Amendment Date: | August 18, 2021 |
| Award Number: | 1744428 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2017 |
| End Date: | September 30, 2022 (Estimated) |
| Total Intended Award Amount: | $99,967.00 |
| Total Awarded Amount to Date: | $99,967.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1608 4TH ST STE 201 BERKELEY CA US 94710-1749 (510)643-3891 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Berkeley CA US 94704-5940 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | INSPIRE |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
A pair of workshops will be conducted to develop innovative ways of incorporating social science findings about the practice of data science and insights from educational research into data science curricula. The workshop series will develop elements of 21st-century data science education anchored in the doing of data science in disciplinary research settings. Workshop participants will form a community of practice with practicing data scientists, educators, and social scientists working together. This project promotes Convergence by integrating insights from social science research and education research in the context of addressing the problem of how to teach data science and develop the appropriate curricular materials.
The first workshop is planned for 1.5 days with about 50 participants, and will strive to identify key insights from social science research and education research into the practice of data science, in the service of their translation into data science curriculum. It will draw on findings from a range of social science disciplines (such as science and technology studies, ethnographic observation of transdisciplinary collaboration, computer supported cooperative work, work on inclusion and diversity in teams, organizational / management theory, systems thinking in design, and the human/technology interface) and educational research and design together with disciplinary data science practitioners and educators. In panels and working sessions, the workshop will draw in participants to frame key themes associated with the doing of data science and develop ways of targeting these to curriculum design. The product from this workshop will be a publicly available white paper identifying core insights for the next stage of work.
In the intervening period between the first and second workshops, the observations and insights from the first workshop will be recorded and refined for use by the second workshop. In the interest of cross-fertilization, the workshop organizers will also make contact with select industry practitioners to assess the importance of the identified concepts for data science translated to real world application.
The second workshop will be for 1 day with about 30 participants. It will outline and construct implementable approaches for translating the key insights into data science curricula. Thematic sessions through the day will build on the taxonomy of key insights developed in the first workshop, and address substantive issues around practical implementation, and prototyping of new curricular forms. The products from this workshop will be a fleshed-out set of curricular elements (such as exercises, course modules, etc.) and best practices guidelines to be publicly disseminated.
These workshops will spotlight the doing of data science as a key arena for collaboratively engaging data science educators and social scientists in curriculum design. This project reorients the transdisciplinary data science education agenda by focusing on practitioners and practice. The workshops will develop implementable, publicly available curricular elements and best practices for data science education oriented to the collaborative doing of data science, with attention to important issues such as diversity, inclusion, and societal and ethical context.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The project team for "Convergence HDR: Social Science Insights for 21st Century Data Science Education" developed and shared college-level curriculum and supporting materials for Data Science education. The team piloted a new Data Science course, "Data and Justice," that drew heavily from the social sciences, and they developed broadly available Data Science curricular materials, such as "The Value of a Home," examining property tax assessments in Cook County, IL.
The project team hosted and participated in a series of workshops, conferences, and online platforms that brought social scientists together with Data Science educators. These included interactive sessions at the National Workshop for Data Science Education, contributions to the Mozilla Foundation's Teaching Responsible Computing Summit and Playbook, and the Academic Data Science Alliance (ADSA)'s Data Science Ethos Lifecycle.
As part of the project, postdoctoral fellow Dr. Hani Gomez developed expertise through focused work in the social sciences and participated in educational design and assesssment. She received mentorship from the PI and the project team.
The project team's work contributed to making Data Science and Computer Science education more socially aware, culturally responsive, and relevant to diverse learners. This will contribute to developing a STEM workforce that will be more attuned to the impact of their work and ideally more representative of U.S. society.
Last Modified: 06/26/2023
Modified by: Cathryn L Carson
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