Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/1569
Title: Data Wrangling Practices and Process in Modeling Family Migration Narratives with Big Data Visualization Technologies
Authors: Jiang, Shiyan
Kahn, Jennifer
Issue Date: Jun-2019
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Jiang, S. & Kahn, J. (2019). Data Wrangling Practices and Process in Modeling Family Migration Narratives with Big Data Visualization Technologies. In Lund, K., Niccolai, G. P., Lavoué, E., Gweon, C. H., & Baker, M. (Eds.), A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019, Volume 1 (pp. 208-215). Lyon, France: International Society of the Learning Sciences.
Abstract: Big data technologies are powerful tools for telling evidence-based narratives about oneself and the world. In this paper, we examine the sociotechnical practices of data wrangling--strategies for selecting and managing datasets to produce a model and story in a big data interface--for youth assembling models and stories about family migration using interactive data visualization tools. Through interaction analysis of video data, we identified ten data wrangling practices and developed a conceptual model of the data wrangling process that contains four interrelated recursive stages. These data wrangling practices and the process of data wrangling are important to understand for supporting future data science education opportunities that facilitate learning and discussion about scientific and socioeconomic issues. This study also sheds light on how the family migration modeling context positioned the youth as having agency and authority over big data.
URI: https://doi.dx.org/10.22318/cscl2019.208
https://repository.isls.org//handle/1/1569
Appears in Collections:CSCL 2019

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