Students' privacy concerns in learning analytics: Model development

Mutimukwe, Chantal; Viberg, Olga; Oberg, Lena‐Maria; Cerratto‐Pargman, Teresa

doi:10.1111/bjet.13234

Cited by 92Privacy, Security, and Data ProtectionOnline Learning and AnalyticsPrivacy-Preserving Technologies in Data

Students' privacy concerns in learning analytics: Model development

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Mutimukwe, Chantal, Viberg, Olga, Oberg, Lena‐Maria, Cerratto‐Pargman, Teresa·2022·British Journal of Educational Technology

DOI 10.1111/bjet.13234

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Abstract

Understanding students' privacy concerns is an essential first step toward effective privacy‐enhancing practices in learning analytics (LA). In this study, we develop and validate a model to explore the students' privacy concerns (SPICE) regarding LA practice in higher education. The SPICE model considers privacy concerns as a central construct between two antecedents—perceived privacy risk and perceived privacy control, and two outcomes—trusting beliefs and non‐self‐disclosure behaviours. To validate the model, data through an online survey were collected, and 132 students from three Swedish universities participated in the study. Partial least square results show that the model accounts for high variance in privacy concerns, trusting beliefs, and non‐self‐disclosure behaviours. They also illustrate that students' perceived privacy risk is a firm predictor of their privacy concerns. The students' privacy concerns and perceived privacy risk were found to affect their non‐self‐disclosure behaviours. Finally, the results show that the students' perceptions of privacy control and privacy risks determine their trusting beliefs. The study results contribute to understand the relationships between students' privacy concerns, trust and non‐self‐disclosure behaviours in the LA context. A set of relevant implications for LA systems' design and privacy‐enhancing practices' development in higher education is offered. Practitioner notes: What is already known about this topic Addressing students' privacy is critical for large‐scale learning analytics (LA) implementation. Understanding students' privacy concerns is an essential first step to developing effective privacy‐enhancing practices in LA. Several conceptual, not empirically validated frameworks focus on ethics and privacy in LA. What this paper adds The paper offers a validated model to explore the nature of students' privacy concerns in LA in higher education. It provides an enhanced theoretical understanding of the relationship between privacy concerns, trust and self‐disclosure behaviour in the LA context of higher education. It offers a set of relevant implications for LA researchers and practitioners. Implications for practice and/or policy Students' perceptions of privacy risks and privacy control are antecedents of students' privacy concerns, trust in the higher education institution and the willingness to share personal information. Enhancing students' perceptions of privacy control and reducing perceptions of privacy risks are essential for LA adoption and success. Contextual factors that may influence students' privacy concerns should be considered.

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Students' Information Privacy Concerns in Learning Analytics: Towards a Model DevelopmentPreprint
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Authors: Mutimukwe, C., Viberg, O., Oberg, L., & Cerratto‐Pargman, T.
Affiliations: Stockholm University (SE)
KTH Royal Institute of Technology (SE)
Department of Computer and Systems Science Mid Sweden University Östersund Sweden
Department of Computer and Systems Science Stockholm University Stockholm Sweden
Corresponding author: Viberg, Olga
ORCID: Mutimukwe, Chantal
Viberg, Olga
Venue: British Journal of Educational Technology
Publisher: Wiley
Published: 2022-05-19
Volume: 53
Issue: 4
Pages: 932-951
ISSN: 0007-1013 (ISSN-L)
1467-8535
DOI: 10.1111/bjet.13234
OpenAlex: W4280591445
Cited by: 90
References: 85
Citations by year: 2026: 28 · 2025: 18 · 2024: 22 · 2023: 18 · 2022: 4
FWCI: 22.30
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor)
doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: British Journal of Educational Technology (publishedVersion, cc-by-nc)
Topics: Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords: learning analytics, privacy risks, institutional trust, higher education, SPICE model, personal data sharing

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Students' privacy concerns in learning analytics: Model development

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