At the Intersection of Self and Society: Learning, Storytelling, and Modeling With Big Data
Kahn, Jennifer Beth
The recent public availability of large-scale datasets, also known as big data, and digital visualization tools has ushered in new ways of telling stories about the social world. The three papers that comprise this dissertation collectively explore how both youth and young adults learn to engage in the interdisciplinary, representational practices that support becoming modelers, storytellers, and consumers of stories told with big data. The first paper is a literature review that introduces storytelling and modeling with big data as a new cultural activity and a rich design space for learning. The second and third papers draw on a corpus of observational studies and design studies of experimental teaching and dive deeply into interaction in each setting to understand participants’ comparative, representational practices for assembling models with big data and dynamic visualization tools. The second paper compares three case studies of storytelling and modeling with big data: a professional big data storyteller from the public media and two groups of newcomers—mathematics and social studies preservice teachers in our design-based research studies—performing stories about global development trends with an interactive, big data visualization tool. The analysis of video records across cases found that getting personal with big data—connecting personal experiences to aggregate trends described in the model—can support telling stories about society that counter, challenge, or critique dominant or conventional social narratives. This work motivated the design study iteration reported in the third paper, which examined storytelling and modeling with big data in a personal context: Teenage youth in the public library were invited to create family data storylines about personal family mobility in relation to national census data trends. The third paper found that scaling personal histories to socioeconomic and historical issues represented by big data entails serious data wrangling to align the family story with the data and supports meaningful forms of learning about oneself, one’s family, and society. Furthermore, locating a population that one identifies with or finding places of meaning in models is an important first step for engagement with big data interfaces.