Award Abstract # 1915714
Undergraduate Data Science Education at Scale

NSF Org: DUE
Division Of Undergraduate Education
Recipient: REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE
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 2019 = $1,893,357.00
FY 2021 = $1,106,643.00
History of Investigator:
  • David Harding (Principal Investigator)
    dharding@berkeley.edu
  • Oliver O'Reilly (Co-Principal Investigator)
  • Rodolfo Mendoza-Denton (Co-Principal Investigator)
  • John DeNero (Co-Principal Investigator)
  • Claudia von Vacano (Co-Principal Investigator)
  • Catherine Koshland (Former Co-Principal Investigator)
  • David Culler (Former Co-Principal Investigator)
  • Cathryn Carson (Former Co-Principal Investigator)
Recipient Sponsored Research Office: University of California-Berkeley
1608 4TH ST STE 201
BERKELEY
CA  US  94710-1749
(510)643-3891
Sponsor Congressional District: 12
Primary Place of Performance: University of California, Berkeley
462 Barrows Hall
Berkeley
CA  US  94720-1980
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): GS3YEVSS12N6
Parent UEI:
NSF Program(s): IUSE
Primary Program Source: 04001920DB NSF Education & Human Resource
04002122DB NSF Education & Human Resource

04002223DB NSF Education & Human Resource

04002324DB NSF STEM Education
Program Reference Code(s): 8209, 9178
Program Element Code(s): 199800
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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Bhalli, Noshaba and Janeja, Vandana and Harding, David "Effects of Prior Academic Experience in Introductory Level Data Science Course" , 2024 https://doi.org/10.1145/3626253.3635505 Citation Details
Chen, Lujie Karen and Thai, Justin "Assessment-via-Teaching: Exploring an Alternative Assessment Strategy in Undergraduate Introductory Data Science Course" , 2024 https://doi.org/10.1145/3626253.3635516 Citation Details
Janeja, Vandana and Sanchez, Maria "RETHINKING DATA SCIENCE PEDAGOGY WITH EMBEDDED ETHICAL CONSIDERATIONS" EDULEARN Proceedings , v.1 , 2022 https://doi.org/10.21125/edulearn.2022.1964 Citation Details
Janeja, Vandana P and Sanchez, Maria and Khoo, Yi Xuan and Von_Vacano, Claudia and Chen, Lujie Karen "Adopting Foundational Data Science Curriculum with Diverse Institutional Contexts" , 2024 https://doi.org/10.1145/3626252.3630771 Citation Details
von Vacano, Claudia and Ruiz, Michael and Starowicz, Renee and Olojo, Seyi and Moreno Luna, Arlyn Y. and Muzzall, Evan and Mendoza-Denton, Rodolfo and Harding, David J. "Critical Faculty and Peer Instructor Development: Core Components for Building Inclusive STEM Programs in Higher Education" Frontiers in Psychology , v.13 , 2022 https://doi.org/10.3389/fpsyg.2022.754233 Citation Details
Wang, Susan and Harding, David and von_Vacano, Claudia "TRANSLATING A LOWER-DIVISION DATA SCIENCE COURSE: LESSONS LEARNED AND CHALLENGES ENCOUNTERED" , 2024 https://doi.org/10.21125/edulearn.2024.1257 Citation Details
Wang, Susan and Janeja, Vandana and Harding, David and Von Vacano, Claudia and Lobo, Daniel "ADOPTING DATA SCIENCE CURRICULA: A STUDENT CENTRIC EVALUATION" INTED Proceedings , v.1 , 2023 https://doi.org/10.21125/inted.2023.2276 Citation Details

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