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Discovering Generative Uncertainty in Learning Analytics Dashboards

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Visualizations and Dashboards for Learning Analytics

Part of the book series: Advances in Analytics for Learning and Teaching ((AALT))

Abstract

The proliferation of learning dashboards in K–12 education calls for deeper knowledge of how such tools fit into existing data use routines in schools. An exemplar routine is coaching cycles, where teachers and experienced coaches collaborate on using data to improve student learning. Within this context, our mixed-method study draws from interviews and think-aloud sessions about dashboard visualizations, conducted with teachers and instructional coaches from four school districts in the United States. Our analyses illuminate how different professional roles express varied patterns of response when facing LA dashboards. The analyses uncover a particular pattern of asking questions to resolve uncertainty that leads to further reflection and action. We discuss how uncertainty towards data and visualizations can be productive for teacher learning and the implications of designing for uncertainty in dashboards. Mapping LA dashboards to educators’ daily routines is important to promote the uptake of analytics towards improving instructional practices.

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grants No. 1719744, 1620851, 1621238, and 1620863. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of the National Science Foundation. We thank all teachers, instructional coaches, and district leaders who participated in this study.

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Correspondence to Ha Nguyen .

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Nguyen, H., Campos, F., Ahn, J. (2021). Discovering Generative Uncertainty in Learning Analytics Dashboards. In: Sahin, M., Ifenthaler, D. (eds) Visualizations and Dashboards for Learning Analytics. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-81222-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-81222-5_21

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