Students' Information Privacy Concerns in Learning Analytics: Towards a Model Development
Chantal Mutimukwe, Jean Damascene Twizeyimana, Olga Viberg

TL;DR
This paper develops a theoretical model to understand students' information privacy concerns in learning analytics, focusing on perceived privacy vulnerability, control, trust, and disclosure behaviors in higher education.
Contribution
It proposes a novel theoretical framework linking privacy perceptions to trust and disclosure, tailored specifically for the learning analytics context.
Findings
Identifies key factors influencing students' privacy concerns in LA
Highlights the importance of perceived control and vulnerability
Provides insights for developing effective privacy practices
Abstract
The widespread interest in learning analytics (LA) is associated with increased availability of and access to student data where students' actions are monitored, collected, stored and analysed. 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 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…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · Privacy-Preserving Technologies in Data · Internet Traffic Analysis and Secure E-voting
