Telling the City With Big Data

• In Event: Reinscribing the City in Design Studies of Critical STEAM Conceptual Practice

Abstract

Introduction. This paper examines how storytelling practices with statistical and mathematical models using “big data” encourage youth to engage in personal, geographical, historical, and mathematical inquiry. Large data sets (aka Big Data) have flooded media, commercial, and political markets, often in the form of performed stories and arguments (Phillips, 2013). Diverse publics can now access big data and sophisticated tools for telling stories that challenge official narratives about people and places. We report on how youth’s relationships to stories told with socio-scientific big data lead to critical forms of spatial thinking, analysis, and modeling.

Frameworks. Our design studies draw on STEM storytelling performances commonly found in public media that rely on statistical reasoning with big data models. We regard STEM modeling environments as opportunities to promote social justice and democracy (Enyedy & Mukhopadhyay, 2007; Gutstein, 2006; Nespor, 2000) and for disciplinary learning that is culturally and historically relevant to learners (e.g., Bang & Medin, 2010). We also view STEM critical literacy practices for interpreting big data representations as central to participation in civic public discourse in and outside of school (Philip, Schuler-Brown, & Way, 2013). Following theories of place as comprised of stories (Massey, 2003), we examine storytelling with big data as a context for developing understandings of place across local and global scales.

Methods. We report on comparative analyses of learning and place-making with big data. The research team led a 2-week human geography course for teenage youth enrolled in a summer academic program at a private university in a mid-sized, Southern US city. The first analysis is of an activity in which students found compelling big data stories from online public media, interpreted and performed those stories to their peers, and then explained how they personally connected to the stories. The second corpus follows a group of students from the class who compared census maps and linked demographic data with qualitative data (e.g., oral histories) as a part of their final project to tell the “untold stories” of how a city’s neighborhoods faired during and after a natural disaster.

We conducted multimodal interaction analysis (Derry et al., 2010) with video and audio records of the youth engaged in these activities to examine talk and embodied interactions with tools and peers and underlying assumptions of their stories.

Findings and Significance. Our designed activities invited students to engage in technical practices with aggregated data and social, economic, and historical issues. Participants laminated data layers onto geographic space as they told meaningful stories, often relating their own neighborhoods and family histories to times and geographies beyond their personal experiences. Scaling between personal and regional, national, or global trends supported complex, critical inquiry and discussion about spatial justice. Additionally, we found that taking a political stance was part of the practice of getting personal with big data for some students. Our study suggests that big data and representational tools that afford scaling across both time and space are rich for public STEM learning.

Authors

• Jennifer Beth Kahn, Vanderbilt University - Peabody College

• Rogers P. Hall, Vanderbilt University

• Alvin Pearman, Vanderbilt Peabody College