Students' privacy concerns in learning analytics: Model development

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. 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.

Affective Support for Self-Regulation in Mobile-Assisted Language Learning

“…This suggests that critical issues of students' privacy, security and ethics should be considered in all cases (for more, see Viberg, Wasson, et al, 2020;Häuselmann, 2021;Ong, 2021). Mutimukwe et al (2022) stress that it is also important to consider contextual factors that may influence students' privacy concerns.…”

Valid questions: the development and evaluation of a new library learning analytics survey

“…Because they provided a validated tool and a description of its creation and development, Whitelock-Wainwright et al's work has been reused in numerous subsequent publications worldwide (Bailey, 2021;Engstr€ om et al, 2022;Garcia et al, n.d.;Pontual Falcão et al, 2022) and has informed other learning analytics surveys (Mahmoud et al, 2022;Mutimukwe et al, 2022). It has additionally been integrated as part of the SHEILA Framework, which used participatory action research to develop student and staff surveys and focus group protocols to support institutions who are developing learning analytics policies or strategies (Gray et al, 2022).…”

Establishing Trustworthy Artificial Intelligence in Automated Feedback

“…To date, many principles and laws (e.g., General Data Protection Regulation) were proposed to protect the users' (e.g., students) information privacy [40]. However, students might have a concern about the data breach of their learning behaviours (e.g., learning materials download frequency and login duration) in the learning management systems controlled by the institutions [14] and this concern can further cause a negative effect to the students' trust [28,27]. When scrutinising the feedback generation process, the systems need to collect and process learning behaviours from students, use AI algorithms to analyse the behaviour patterns (e.g., students' engagement level with the course materials), and generate personalised feedback to the students [10,8].…”

“…Therefore, there might be a conflict between the feedback generation process and the students' privacy concerns regarding their learning behaviour data. To alleviate the students' privacy concerns, as suggested by the work [28], the institutions might consider providing an opportunity for students to review how their data was collected and used to generate the feedback, training the students to enhance their perception on protecting their private information, and allowing students to determine the collection and use of their data.…”