Harding leads NSF Project on Undergraduate Data Science Education

Professor David Harding is the Principal Investigator of a new 5-year $3 Million NSF-funded project titled, "Undergraduate Data Science Education at Scale," that will evaluate, refine, and disseminate UC Berkeley's innovative data science undergraduate education program. The project, a joint effort of the Social Science D-Lab, the Data Science Education Program and Lawrence Hall of Science, includes Co-PIs Cathryn Carson, Cathy Koshland, David Culler, and Rudy Mendoza-Denton and involves a collaboration with Elisabeth Wade of Oakland's Mills College and Vandana Janeja of the University of Maryland Baltimore County. Those universities will implement their own versions of the Berkeley program that are tailored to their student populations and campus institutions to ensure that the program will work in a wide variety of colleges and universities. Lynn Uyen Tran, Research Director of the Learning & Teaching Group at the Lawrence Hall of Science will lead the project's evaluation team. 

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.