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Title: Appearing and Disappearing in the Data: Emotional Configurations Within Children’s Data Modeling Practices
Authors: Lanouette, Kathryn
Church, Megan
Maynard, Courtney
Keywords: Learning Sciences
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Lanouette, K., Church, M., & Maynard, C. (2023). Appearing and disappearing in the data: Emotional configurations within children’s data modeling practices. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 1402-1405). International Society of the Learning Sciences.
Abstract: Data modeling is a central science knowledge building practice, entailing amplifying and reducing the world across inscriptional forms. Recent research in science education has elevated how emotion is integral to science knowledge building practices, yet emotion emergent within young people’s data construction and critique processes remains less understood. In this study, we present findings from a multi-week 5th grade science and data science curriculum. We describe how emotion was often emergent as aspects of the ecological system were made more or less visible - by the data form’s structure as well as by the activity structure. We focus in depth on one illuminating case were children falsify their data, detailing how emotion, sensemaking and modeling practices were co-emergent. Building on existing scholarship in data feminism, our findings suggest that transforming the world into data is an inherently emotional endeavor, in turn entangled in young peoples’ sensemaking with and about data.
Description: Short Paper
Appears in Collections:ISLS Annual Meeting 2023

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