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Students’ information privacy concerns in learning analytics: Towards model development
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-8543-3774
2021 (English)In: Nordic Learning Analytics (Summer) Institute 2021: Proceedings of the Nordic Learning Analytics (Summer) Institute Stockholm, Sweden, August 23, 2021, CEUR-WS , 2021, Vol. 2985Conference paper, Published paper (Refereed)
Abstract [en]

The widespread interest in learning analytics (LA) is associated with increased availability of and access to student data where students’ actions are monitored, recorded, stored and analyzed. The availability and analysis of such data is argued to be crucial for improved learning and teaching. Yet, these data can be exposed to misuse, for example, to be used for commercial purposes, consequently, resulting in information privacy concerns (IPC) of students who are the key stakeholders and data subjects in the LA context. The main objective of this study is to propose a theoretical model to understand the IPC of students in relation to LA. We explore the concept of IPC as a central construct between its two antecedents: perceived privacy vulnerability and perceived privacy control, and its consequences, trusting beliefs and self-disclosure behavior. Although these relationships have been investigated in other contexts, this study aims to offer mainly theoretical insights on how these relationships may be shaped in the context of LA in higher education. Understanding students’ IPC, the related root causes and consequences in LA is the key step to a more comprehensive understanding of privacy issues and the development of effective privacy practices that would protect students’ privacy in the evolving setting of data-driven higher education.

Place, publisher, year, edition, pages
CEUR-WS , 2021. Vol. 2985
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2985
Keywords [en]
Higher education, Information privacy concerns, Learning analytics, Model development, Students, Education computing, Learning systems, Data subjects, Exposed to, High educations, Learning analytic, Learning and teachings, Privacy control, Theoretical modeling, Trusting beliefs
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-312857Scopus ID: 2-s2.0-85118228501OAI: oai:DiVA.org:kth-312857DiVA, id: diva2:1660400
Conference
2021 Nordic Learning Analytics (Summer) Institute, NLASI 2021, 23 August 2021, Stockholm, Sweden
Note

QC 20220524

Available from: 2022-05-24 Created: 2022-05-24 Last updated: 2022-06-25Bibliographically approved

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Chantal, MutimukweViberg, Olga

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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