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Title
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Co-Designing for Privacy, Transparency, and Trust in K-12 Learning Analytics
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Abstract/Description
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The process of using Learning Analytics (LA) to improve teaching works from the assumption that data should be readily shared between stakeholders in an educational organization. However, the design of LA tools often does not account for considerations such as data privacy, transparency and trust among stakeholders. Research in human-centered design of LA does attend to these questions, specifically with a focus on including direct input from K-12 educators. In this paper, we present a series of design studies to articulate and refine conjectures about how privacy and transparency might influence better trust-building and data sharing within four school districts in the United States. By presenting the development of four sequential prototypes, our findings illuminate the tensions between designing for existing norms versus potentially challenging these norms by promoting meaningful discussions around the use of data. We conclude with a discussion about practical and methodological implications of our work to the LA community.
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Date
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2021
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In publication
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LAK21: 11th International Learning Analytics and Knowledge Conference
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Series
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LAK21
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Pages
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55–65
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Publisher
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Association for Computing Machinery
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IRE Approach/Concept
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Co-Design
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Learning Analytics
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Open access/full-text available
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en
Yes
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Peer reviewed
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en
Yes
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ISBN
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978-1-4503-8935-8
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Citation
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Ahn, J., Campos, F., Nguyen, H., Hays, M., & Morrison, J. (2021). Co-Designing for Privacy, Transparency, and Trust in K-12 Learning Analytics. LAK21: 11th International Learning Analytics and Knowledge Conference, 55–65. https://doi.org/10.1145/3448139.3448145
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Place
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New York, NY, USA
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