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 participantsâ 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 newcomersâmathematics and social studies preservice teachers in our design-based research studiesâperforming 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 dataâconnecting personal experiences to aggregate trends described in the modelâcan 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, oneâs 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.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-11172017-135209 |
| Date | 22 November 2017 |
| Creators | Kahn, Jennifer Beth |
| Contributors | Rogers Hall, Richard Lehrer, Andrew Hostetler, Thomas Philip |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-11172017-135209/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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