Visualizing Inequity: How STEM Educators Interpret Data Visualizations to Make Judgments About Racial Inequity

Data visualizations are routinely used for STEM faculty development to support equitable teaching practices. Yet, little is known about how instructors interpret such data visualizations. This interview study fills a key gap by providing insight into how STEM educators make sense of visualizations. We report on cognitive interviews with 17 participants who were shown eight different data visualizations depicting racial inequities in classroom participation. The participants were asked to interpret whether the scenarios were equitable and answer questions about the distribution of participation. We report on which visualizations participants were able to interpret most accurately, and how particular visualizations supported thinking about inequity. No single visualization was most effective in all cases, and critically, we found that not all visualizations were equally effective for identifying inequities, and that different types of visualizations drew attention to different aspects of inequity (e.g., individual disparities vs. group-level disparities). We also provide data on how participants differentiated between equity and equality. Thus, the present study provides useful information for professional developers about which types of visualizations may be most effective for different purposes and highlights the need for multiple representations of racial inequity. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.