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Title: Remixing as a Key Practice for Coding and Data Storytelling
Authors: Yalcinkaya, Rabia
Sanei, Hamid
Wang, Changzhao
Zhu, Li
Kahn, Jennifer
Jiang, Shiyan
Keywords: CSCL
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Yalcinkaya, R., Sanei, H., Wang, C., Zhu, L., Kahn, J., & Jiang, S. (2022). Remixing as a key practice for coding and data storytelling. In Weinberger, A. Chen, W., Hernández-Leo, D., & Chen, B. (Eds.), Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning - CSCL 2022 (pp. 407-410). International Society of the Learning Sciences.
Abstract: In this study, we investigated high school youth’s computational data literacy practices, focusing on remixing in building interactive data visualizations for telling stories about climate change. Our interaction analysis revealed remixing processes demonstrating how students navigated the code, data, visualization, and story. We illustrate and discuss one of these processes, wayfinding the code, in which youth learned the code structure and the relationship between the code and data visualization through collaboratively locating codes for remixing. These findings shed light on learning designs for fostering data wrangling, building dynamic data visualizations, and data storytelling about socioscientific issues.
Description: Short Paper
Appears in Collections:ISLS Annual Meeting 2022

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