Modeling Social Justice with Digital Tools: Preservice Teachers’ Statistical Investigations Using CODAP

In this study, we investigate how thirty-four preservice teachers (PSTs) engaged in statistical modeling using a socially relevant dataset within a technology-supported environment. We used the statistical investigative cycle and the corresponding social justice cycle (Casey et al., 2023) as a lens to make sense of the PSTs’ work. Data sources included PSTs’ reports generated during a traffic stop investigation module implemented across multiple semesters. PSTs engaged in iterative cycles of descriptive and informal inferential analysis to evaluate claims about racial biases in policing. Our findings highlight the constant interaction between the context and stages of the statistical investigative cycle. Insights from multiple technology-supported statistical investigative cycles informed the social justice cycle. Our work highlights how tasks that merge authentic social justice issues with dynamic data analysis tools can simultaneously support the development of statistical modeling and statistical argumentation competencies among PSTs, while also normalizing issues of social justice in mathematics and statistics classrooms.