Counter-Modeling With Open Large Data Sets as a Form of Critical Comparative Inquiry With Preservice Social Studies Teachers (Poster 3)

• In Event: Exploring Critical Data Literacy in K–12 Social Studies

Abstract

We report from a qualitative analysis of a design based research iteration that invited preservice, secondary social studies teachers (undergraduate and graduate) in a university Human Geography course to participate in storytelling about models made with open large datasets (S’MOLDS). Our study takes an approach to storytelling drawing on linguistic anthropology and microsociology to help us see storytelling both as a form and a performance (e.g., Labov, 1972; Ochs et al., 1992) and perspectives that look at how storytelling and data visualization come together as a developing cultural activity (e.g., Wertsch, 1998).
Over three 3-hour classes over 4 weeks in the middle of the semester, we asked students (n=14) to do three primary tasks: to forage (Vogelstein et al., 2019) and critically analyze found stories about models made with open large datasets in the public media as form of public discourse (Class 1); to remix and share existing S’MOLDS performances from the public media using an extant open data repository and visualization tool for public use (Hans’ Rosling’s TED Talks using Gapminder; Class 2); and then, in groups, to build and perform stories about global health and wealth using Gapminder (Class 3). Using interaction analysis methods (Jordan & Henderson, 1994), we analyzed video records of students' performances (six in total) into Stories About Models (SAMs) units. Within each SAMs unit, we noted instances of counter-modeling, when the students, as tellers, invited their audience to take stances or shifts in footing (Goffman, 1981) in their stories about models to challenge conventional understandings, held by their peer audience and a larger public.
Our analysis students’ S’MOLDS performances suggests that students engaged in counter-modeling as a form of critical comparative inquiry in ways that (a) reflected their disciplinary perspectives as preservice social studies teachers; (b) relied on “getting personal” or making personally relevant connections to aggregate data; and (c) problematized “the power of data” (D'ignazio & Klein, 2020) for telling stories about the world and the realist stance of the data tools in use (i.e., what is measured is real, and data standards and their investments are unproblematic “recipes for reality” [Busch, 2011]). We provide illustrations from across the student cases of counter-modeling: For example, one student group, in telling the story of the world’s worst CO2 polluter, asked fellow students to consider taking personal responsibility for carbon emissions, arguing that the “iPhones in our pockets” generated CO2 that should be attributed to the US, not where they were produced; another student group told a deceptive story about a model made with highly selective data to frame the activity of S’MOLDS as potentially misleading and risky for public audiences (and their future students). Finally, we consider how designs for S’MOLDS activities could be a space for developing critical data literacies for social studies teachers and their future students by inviting learners to engage in counter-modeling with open large datasets as a transdisciplinary way to ask critical questions about social development and data generated to tell stories about society (Becker, 2007).

Authors

• Jennifer Beth Kahn, University of Miami

• Rogers P. Hall, Vanderbilt University