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|Title:||Collaborative Data Engineering: Strategies to Support Macro-level Exploration of Youth Learning Ecosystems|
Martin, Caitlin K.
|Publisher:||International Society of the Learning Sciences|
|Citation:||Pinkard, N., Martin, C. K., & Jones, U. (2021). Collaborative Data Engineering: Strategies to Support Macro-level Exploration of Youth Learning Ecosystems. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 753-756). Bochum, Germany: International Society of the Learning Sciences.|
|Abstract:||The time has come for the learning sciences to expand design and research methods in order to understand access and participation to learning opportunities at a macro level. Advances in technology allow networked social systems (such as out-of-school providers) to crowdsource and combine the data they collect and interpret collectively through data representations. Learning sciences can leverage networked data as a way to stress test theories based on ethnographic research with individuals or small groups and offer learning science perspectives to how we understand and address power-related infrastructure and historic racism. In this paper, we share a process for designing technical systems, refining data models, visualizing learning landscapes, and working with decision makers to impact change using an investigation of the 2019 Chicago summer learning landscape as a case example.|
|Appears in Collections:||ISLS Annual Meeting 2021|
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