The Intersection of Race, Data, and Learning: Applying QuantCrit to Mathematics Education Research
Year: 2025
Type: editorial
Abstract: Mathematics education research has long relied on traditional quantitative methodologies that often obscure systemic inequities. This editorial advocates for the integration of Quantitative Critical Race Theory (QuantCrit) to critically examine the racialized dimensions of mathematical learning, assessment, and policy. QuantCrit challenges the assumed neutrality of data interrogates deficit-based narratives and calls for the use of quantitative methods that advance social justice. By applying QuantCrit, researchers can uncover how structural barriers shape student outcomes and reframe assessment practices to better support historically marginalized learners. This editorial highlights key tenets of QuantCrit, explores its implications for mathematics education, and provides actionable strategies for scholars and educators to incorporate this framework into research and practice. A shift toward justice-oriented quantitative methodologies is necessary to ensure mathematics education fosters equity and inclusion. This work urges researchers and policymakers to engage with QuantCrit as a transformative tool for reimagining mathematics education.
Institution: Texas A&M University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4408682201