Interpretations and Uses of Data for Equity in Computing Education

Extended Abstracts of the 2021 ACM Conference on International Computing Education Research

My submission and poster for the ICER 2021 Doctoral Consortium provided an overview of the three main projects of my dissertation. Combined, these projects are design explorations of how stakeholders (students, teachers, curriculum designers) can interpret and use data to support equity-oriented goals.


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.