Making Data Work: Using Conversation and Interaction Analysis to Study Data Science Education

ABSTRACT This article presents a methodological approach that combines conversation analysis (CA) and interaction analysis (IA) to examine how students reason with data in collaborative settings, using food justice as an illustrative case. While traditional analytical approaches in data science education often rely on individual cognitive measures or final products, this approach captures the dynamic, interactional nature of data reasoning as it unfolds in real time. By analyzing video‐recorded interactions, we demonstrate how CA/IA can reveal the micro‐processes through which students negotiate meaning, challenge interpretations, and engage with social and ethical dimensions of data. Though demonstrated here in a food justice context, the approach is broadly applicable to any setting where understanding the social organization of collaborative data reasoning matters, including formal classrooms, workplace teams, and community‐based learning environments, and contributes both methodological and practical insights for data science education researchers.