Traffic stops remain the most common interactions between people and the police, and have been contentious for drivers who are Black, Indigenous, and people of color (BIPOC). As such we developed this module with the expectation that Learners1 will have opportunities to make connections between their experiences, analysis, and interpretation, and systemic features (e.g., court rulings) that will facilitate systemic thinking. Learners are likely familiar with traffic stops as they may have been stopped, or know someone who has been stopped. Traffic stops datasets are also freely available for many cities around the country, which means that this module could potentially be adapted to local contexts based on where it is being taught.Our selection of the context was purposeful. Examining the data can reveal the disproportion between Black and white drivers in stops made and searches conducted that cannot be explained away with the bias of individual officers. Our goal is for Learners to see these broader patterns in their investigations and think of why these disparities exist.
Overview of the Module
This module guides Learners through the steps of the statistical investigation process (pose questions, consider data, analyze data, and interpret data) with a dataset that is a random sample taken from 123,963 traffic stops conducted in Charlotte, North Carolina between January 2020 and October 2021 (Note that even though Charlotte data will be used in the instructor guide, similar data for other cities can be used). Variables included in the dataset are mostly categorical. Learners begin by learning about the statistical investigation process and carry out exploratory data analysis (Day 1 & 2), followed by learning how to use simulations to carry out investigations using informal inferential reasoning, and interpret the data (Day 3 & 4). Discussions on whether there is evidence of racial bias in the data will be included throughout the module. Finally on the last day Learners write a report synthesizing what they learned and add recommendations they would give to a city council (Day 5). It is assumed that each day is a 90-minute learning session. Adjustments would have to be made for shorter or longer sessions (e.g., combine Days 1 & 2 for first week and Days 3 & 4 for second week, with day 5 taking up part of a third week or being assigned as homework).
Note that a central goal of the module is to facilitate meaningful conversations informed by data about systemic racism, within the context of traffic stops.
The purpose of the instructor guide is to provide instructors with resources and pedagogical considerations focused on exploring systemic racism with PSTs through the context of traffic stops. Because of the emphasis on situating the instructor within this context, instruction on traditional statistics concepts will be left up to the instructor’s discretion, though some attention is provided to concepts such as exploratory data analysis and inferential statistics within the module. Our arguments draw from the broader analysis of Baumgartner et al. (2018) based on 20 million traffic stops in North Carolina. They tie the disparities in the traffic stops back to the laws about policing; the two main ones being Terry vs Ohio (1968) and Whren vs U.S. (1996).
The guide has instructor resources that include pedagogical suggestions, resources to learn more about the issues around traffic stops, the slides and materials for each day with instructions. The guide also has investigation briefs that outline possible routes that the Learners could take in the investigation and how instructors could anticipate and provide guidance to support Learners making connections to the system. We outline the flow of the module over the days. Note that all the links are embedded in the Instructor Guide.
Outline
To keep the focus on systemic racism throughout the module, there is an overarching research question that guides the work: Is there evidence of racial bias in traffic stops in Charlotte NC?
| Prior to Day 1 | Learners complete the pre-module survey to help the instructor gain an understanding of the experiences Learners have had with this particular issue and their comfort levels. The pre-module survey can also be optional. Learners also complete a pre-assignment reading that will help set the stage for the discussion and exploration on Day 1. |
| Day 1 | Learners start with a discussion around the reading and are then introduced to the dataset in CODAP. The Learners begin by considering the data. They focus on the variables in the dataset and how they are measured, especially race. Guided by the overarching research question, Learners design statistical questions that they will explore with the data. After feedback, Learners revise their statistical questions that will guide their analysis and interpretation. |
| Day 2 | Learners share their statistical questions with their peers and start with descriptive statistics to answer their question. After analyzing the data and interpreting their results within the context. They engage in a class discussion as they share their results and interpretations. For homework, the Learners look at two briefs related to the rulings of Terry vs Ohio and Whren vs United States as they seek to make broader connections between their results and the laws. |
| Day 3 | Learners do the preliminary work related to the design of a simulation that will mimic the traffic stops. Learners start with the Census data for the city as they plan their design. The instructor guides the Learners through an example of a simulation in class and for homework they design a simulation for the traffic stops. |
| Day 4 | Learners share their plans for their simulations and then engage with their simulations. They share their results and interpretations, which leads to a broader discussion about the reasons for the observed disparity in the proportion of Black drivers stopped. Learners conclude if there is racial bias in policing, or not. |
| Day 5 | Day 5 is Learner driven as they explore another statistical question, related to the overall research question, using simulations. After sharing their findings and interpretations with the class, the Learners look back over their work and write a report for the City Council about their findings related to racial bias in the policing of traffic stops and suggest measures for improvement. |
1. Though we designed and tested the modules with preservice and in service teachers, we envision the materials could also be adapted for use by K-16 students.
Licensing
All of the materials shared here are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. This means you are welcome to adapt and share these materials! If you do, please make clear that your materials are adapted from this curriculum and link back to the original materials. We suggest the following language: “These materials are adapted by [Your Name] from the [Module Name] from the project Designing Modules to Support the Development of Mathematics Pre-Service Teachers’ Critical Consciousness through Statistical Investigations of Systemic Racism. You can request the original unit here.”
This project is funded by the National Science Foundation, grant # 2121364. Any opinions, findings, and conclusions or recommendations expressed in these materials are those of the authors and do not necessarily reflect the views of the National Science Foundation.
