Transparency and Consent: Student Perspectives on Educational Data Analytics Scenarios
Abstract
Higher education data mining and analytics, like learning analytics, may improve learning experiences and outcomes. However, such practices are rife with student privacy concerns and other ethics issues. It is crucial that student privacy expectations and preferences are considered in the design of educational data analytics. This study forefronts the student perspective by researching three unique futurized scenarios rooted in real-life systems and practices. Findings highlight student acceptance of data mining and analytics with particular limitations, namely transparency about analytics and consent mechanisms. Without such limitations, institutions risk losing their students' trust.
Additional Information
© 2023 Johns Hopkins University Press. This project was made possible in part by the Institute of Museum and Library Services (LG-96-18-0044-18). The views, findings, conclusions or recommendations expressed in this conference proceeding do not necessarily represent those of the Institute of Museum and Library Services. The team thanks its research assistants for their support: Amy Martin (Indiana University-Indianapolis), Arudi Masinjila (Northwestern University), Margaret McLaughlin (Indiana University-Bloomington), and Claudia Wald (CUNY). Finally, the team thanks the undergraduate students who volunteered their time to participate in this study.Attached Files
Accepted Version - Jones_2023_TransparencyConsent_AcceptedVersion.docx
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Additional details
- Eprint ID
- 122221
- Resolver ID
- CaltechAUTHORS:20230711-164456715
- Institute of Museum and Library Services
- LG-96-18-0044-18
- Created
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2023-07-11Created from EPrint's datestamp field
- Updated
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2023-08-08Created from EPrint's last_modified field