Computing education’s booming enrollment exacerbates inclusion challenges ranging from tools that do not support diverse learners to instructors not being aware of unique challenges that students of minoritized groups face. While data often perpetuates inequities in many contexts, it could also serve to support equity-related goals if properly contextualized. To understand how data could support equitable learning, I explore how affording information and agency supports students’ self-directed learning of Python programming, how contextualizing psychometric data on test bias with curriculum designers’ domain expertise could support equitable curriculum improvements, and how contextualizing student feedback with demographic information and peer perspectives could help instructors become aware of challenges that students from minoritized groups face while preserving student privacy and well-being. By studying how students, curriculum designers, and teachers interpreted and used data relating to experiences learning computing, I contribute techniques that contextualize equity-oriented interpretations and uses of data with stakeholders’ domain expertise.

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Benjamin “Benji” Xie (pronounced ‘she’ + ‘eh’) is a PhD candidate at the University of Washington Information School, advised by Prof. Amy J. Ko. He designs and evaluates ways to use data to support equity and justice in educational contexts. His doctoral research explores interpretations and uses of data for equity in computing education. He publishes his work to computing education and human-computer interaction communities.

He is a National Science Foundation (NSF) Graduate Research Fellow and was previously an MIT EECS-Google Research and Innovation Scholar during his time at MIT researching with Prof. Hal Abelson and MIT App Inventor. He received Master’s and undergraduate degrees in computer science from MIT.