Students' Information Privacy Concerns in Learning Analytics: Towards a Model Development (2109.00068v1)

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 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.