
NSF Org: |
DUE Division Of Undergraduate Education |
Recipient: |
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Initial Amendment Date: | August 19, 2019 |
Latest Amendment Date: | April 12, 2024 |
Award Number: | 1915714 |
Award Instrument: | Continuing Grant |
Program Manager: |
Mike Ferrara
mferrara@nsf.gov (703)292-2635 DUE Division Of Undergraduate Education EDU Directorate for STEM Education |
Start Date: | October 1, 2019 |
End Date: | September 30, 2025 (Estimated) |
Total Intended Award Amount: | $3,000,000.00 |
Total Awarded Amount to Date: | $3,000,000.00 |
Funds Obligated to Date: |
FY 2021 = $1,106,643.00 |
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: |
462 Barrows Hall Berkeley CA US 94720-1980 |
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): | IUSE |
Primary Program Source: |
04002122DB NSF Education & Human Resource 04002223DB NSF Education & Human Resource 04002324DB NSF STEM Education |
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): | 47.076 |
ABSTRACT
With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this project aims to serve the national interest by improving undergraduate data science education for STEM and non-STEM majors. It plans to achieve this goal by implementing, refining, and expanding an innovative prototype data science program at the University of California Berkeley (an R1 university), the University of Maryland, Baltimore County (an R2 university) and Mills College (a primarily women's liberal arts college). The prototype program serves as an entry point into data science for students with limited previous experience in statistics or data science. It is built around a zero-prerequisites data science course, with concurrent connector courses that introduce how data science is used in different fields. It includes modules that "push" data science into existing courses and Discovery Projects that enable students to apply data science skills in real-world settings. It also incorporates a Data Science Scholars program to support student success, particularly students from groups underrepresented in STEM. The prototype program uses a peer instruction model to support student learning, build community, provide mentoring, and co-create course materials with faculty. The project will produce a set of open source curricular materials and the technical infrastructure to facilitate successful implementation of the prototype program at other institutions. It is expected that the models and materials developed through this project will support the teaching of data science at scale to a diverse set of students in diverse types of institutions.
Because data science is a comparatively new field, much work needs to be done to investigate how pedagogical and curricular approaches function in this domain. This project aims to generate new knowledge about how to best design data science curricula and pedagogy to promote learning among diverse undergraduate students, including students from underrepresented groups in STEM. The project's research objectives include evaluation of how specific components of the prototype program impact student outcomes; and assessment of whether and how the prototype can broaden participation in data science. The project's mixed-methods evaluation will include formative evaluation to enable continuous quality improvement, as well as summative evaluation to measure project outcomes. The project will develop curricular and pedagogical data science materials and technical infrastructure that can be efficiently tailored and scaled at different institutions with diverse student bodies and disparate resources. The materials and research findings will be widely disseminated, to help drive a community transformation in undergraduate data science education that can scale with student demand and ultimately broaden participation in data science across multiple, diverse institutional settings. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Institutional and Community Transformation track, which supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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