Toward a Framework for Justice-Oriented Data Science Education in K–12 Schools

• In Event: Creating Equity-Oriented Learning Environments

Abstract

Data science is increasingly relevant to daily life and has garnered significant attention in education. While data science education has been traditionally focused on technical training, justice considerations are increasingly brought up given growing concerns over fairness and justice in data science. This paper introduces a framework for justice-oriented data science education that comprises five areas grounded in a broad range of literature. To explore and refine the framework in authentic contexts, we applied it to discourse data from one participatory design workshop with teachers. Analysis demonstrated the presence of this framework’s areas and their rich connections in teachers’ thinking. The framework offers educators a tool to integrate data science, justice issues, and disciplinary content in K-12 classrooms.

Authors

• Bodong Chen, University of Pennsylvania
  Presenting Author

• John Bartucz, University of Minnesota
  Presenting Author

• Cassandra Scharber, University of Minnesota
  Non-Presenting Author

• Vimal V. Rao, University of Minnesota
  Non-Presenting Author

• David DeLiema, University of Minnesota
  Non-Presenting Author