Students' privacy concerns in learning analytics: Model development

C Mutimukwe, O Viberg, LM Oberg… - British Journal of …, 2022 - Wiley Online Library
British Journal of Educational Technology, 2022Wiley Online Library
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 …
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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