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Title: Data Literacies and Social Justice: Exploring Critical Data Literacies through Sociocultural Perspectives
Authors: Irgens, Golnaz Arastoopour
Simon, Knight
Wise, Alyssa
Philip, Thomas
Olivares, Maria C.
Van Wart, Sarah
Vakil, Sepehr
Marshall, Jessica
Parikh, Tapan S.
Lopez, M. Lisette
Wilkerson, Michelle
Gutiérrez, Kris
Jiang, Shiyan
Kahn, Jennifer
Keywords: Learning and Identity
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Irgens, G. A., Simon, K., Wise, A., Philip, T., Olivares, M. C., Van Wart, S., Vakil, S., Marshall, J., Parikh, T. S., Lopez, M. L., Wilkerson, M., Gutiérrez, K., Jiang, S., & Kahn, J. (2020). Data Literacies and Social Justice: Exploring Critical Data Literacies through Sociocultural Perspectives. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 1 (pp. 406-413). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: The ability to interpret, evaluate, and make data-based decisions is critical in the age of big data. Normative scripts around the use of data position them as a privileged epistemic form conferring authority through objectivity that can serve as a lever for effecting change. However, humans and materials shape how data are created and used which can reinscribe existing power relations in society at large (Van Wart, Lanouette & Parikh, 2020). Thus, research is needed on how learners can be supported to engage in critical data literacies through sociocultural perspectives. As a field intimately concerned with data-based reasoning, social justice, and design, the learning sciences is well-positioned to contribute to such an effort. This symposium brings together scholars to present theoretical frameworks and empirical studies on the design of learning spaces for critical data literacies. This collection supports a larger discussion around existing tensions, additional design considerations, and new methodologies.
Appears in Collections:ICLS 2020

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