Goodness of Fit: Teachers’ Boundary Work at the Intersection of Data Science and Mathematics Education

Abstract

School mathematics in the U.S. is shaped by competing and contradictory goals that reflect broader tensions around the purpose of public education: whether it should serve democratic or socioeconomic aims. These tensions have created a landscape in which mathematics often functions as a sorting mechanism and gatekeeper, positioning students in hierarchies of academic and social value. Despite ongoing reform efforts, mathematics instruction continues to be shaped by these logics of efficiency, standardization, and quantification. In this context, data science has been increasingly positioned as a modern, relevant alternative to traditional mathematics. However, data science is not a neutral alternative and carries its own epistemologies, histories, and risks. This dissertation takes up the question of how teachers make sense of and integrate data practices in ways that they see as relevant, meaningful, and equitable within the structural and ideological constraints of school mathematics. This qualitative study examines how four secondary mathematics teachers navigated the everyday instructional and ideological work of integrating data practices. Data include professional development sessions, classroom video and artifacts, lesson plans, and interviews. Findings illustrate how teachers worked to use data to recontextualize mathematics – positioning data as a means to surface inquiry, contextualize mathematical ideas, and connect to students’ lived experiences. Their lesson designs reflected different orientations to disciplinary integration, using data to either situate, serve, or reshape mathematical goals. In classrooms, teachers engaged in boundary work that revealed tensions between mathematical precision and interpretive judgment. These tensions created moments of both constraint and possibility as teachers attempted to hold space for student reasoning within the dominant logics of school mathematics. Across their decisions, teachers’ pedagogical commitments shaped what kinds of participation and meaning-making were possible, and reflected broader efforts to design mathematics instruction that was relevant, meaningful, and humanizing. This dissertation offers design principles for integrating data and mathematics that attend to disciplinary tensions, support interpretive reasoning, and center equity. It contributes to theory-building in data science education by situating teacher learning within the sociohistorical and political landscape of mathematics education.