Gendered and Racialized Barriers to Persistence in Undergraduate Data Science Education

A D-Lab special event

with Claudia Natalia von Vacano, Executive Director and Senior Research Associate, D-Lab and Digital Humanities at UC Berkeley, and David J. Harding, Professor, Sociology, UC Berkeley, and Faculty Director, D-Lab

Professor David J. Harding and Dr. Claudia Natalia von Vacano present findings from “Gendered and Racialized Barriers to Persistence in Undergraduate Data Science Education: A Mixed-Method Study of Growth Mindset, Belonging, and Self-Efficacy.” Drawing on administrative records, surveys, and interviews from a large R1 program, they show that Black and Latina women are significantly less likely than White and Asian men to continue beyond the foundational course, not because of weaker growth mindsets but due to a lower sense of belonging and self-efficacy. The talk highlights how isolation and microaggressions undermine persistence and outlines actionable strategies, such as community-building, inclusive pedagogy, and targeted mentorship, to create psychologically safe learning environments and improve equity in data science pathways.

The Berkeley Data Science Education Fellowship is a two-year postdoctoral program designed to address the critical need for research on effective, ethical, and inclusive approaches to teaching data science. It will support three fellows who will first gain broad experience by apprenticing with multiple research projects before designing and conducting their own independent research in the second year. The ultimate goal is to train a new generation of interdisciplinary experts capable of tackling systemic issues like student retention and equity. This aims to improve large-scale data science curricula to be more socially and culturally relevant for diverse student populations. Importantly, PI Michelle Wilkerson is bringing data science education researchers together in conversation to synthesize the field.

Accessibility

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