The Berkeley Data Science Education Fellowship: Exploring Ethical and Inclusive Approaches to Data Science in a Shifting Landscape

This project will support a cohort of three postdoctoral fellows for two years as they study Data Science Education at The University of California, Berkeley and partner educational institutions. The College of Computing, Data Science, and Society houses a variety of curricular and educational initiatives that offer many opportunities to study pedagogy, educational trajectories, and ethics and inclusion in Data Science.

Fellows will be recruited from diverse institution types and disciplinary backgrounds to conduct research on Data Science Education through first apprenticing with multiple research and curriculum development projects, and then designing their own independent research project with support from a mentorship team. Fellows will develop expertise in generating high-quality Data Science Education research and curriculum by working with educators across precollege, 2-year, and 4-year contexts; addressing equity and inclusion in Data Science Education spaces; and examining curriculum and instruction focused on teaching computing and data science to diverse audiences.

The rise of data-rich computing has had rippling effects across educational sectors. Since computing and data science are highly contextual and have clear social applications and social impacts, many have argued that they can serve as an integrative thread for diversifying Science, Technology, Engineering, and Mathematics broadly. However, a lack of academic preparation, and of curricular and instructional support, can present roadblocks especially for marginalized populations to enter the field.

Despite these challenges, most empirical work in Data Science Education has focused on the design and evaluation of courses or major programs of study, rather than these deeper systemic issues related to student preparation, learning and retention. The Berkeley Data Science Education Fellowship will support training and research toward a coherent, interdisciplinary, mixed-methods approach to studying Data Science Education at a system level.

The Fellowship will prepare three postdoctoral researchers to conduct high-quality social sciences research in complex contexts, in collaboration with interdisciplinary colleagues including domain experts and educators. In Year 1, Fellows will be provided broad exposure to Data Science curricular and educational initiatives as research apprentices with research projects that employ different methodologies and work with different student audiences.

In Year 2, Fellows will commit to a specific project to complete a longer-term research internship, as well as designing and enacting an independent research project. Fellows will also participate in monthly community-building events and colloquia, teach Data Science workshops and/or courses, and receive professional development feedback and advice from a cadre of interdisciplinary mentors.

Through these research and teaching experiences, Fellows will gain comfort and competency in research and development of large-scale, introductory technical Data Science courses and workshops, with an eye toward social and cultural relevance.

This project is funded by the Science, Technology, Engineering, and Mathematics (STEM) Education Postdoctoral Research Fellowship Program (STEM Ed PRF) with co-funding from the Improving Undergraduate STEM Education Program (IUSE:EDU) Program. The STEM Ed PRF Program aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field.

The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students.

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.