Learning analytics at the intersections of student trust, disclosure and benefit

Slade, Sharon; Prinsloo, Paul and Khalil, Mohammad (2019). Learning analytics at the intersections of student trust, disclosure and benefit. In: LAK19 Proceedings of the 9th International Conference on Learning Analytics & Knowledge, ACM Press pp. 235–244.

DOI: https://doi.org/10.1145/3303772.3303796


Evidence suggests that individuals are often willing to exchange personal data for (real or perceived) benefits. Such an exchange may be impacted by their trust in a particular context and their (real or perceived) control over their data.

Students remain concerned about the scope and detail of surveillance of their learning behavior, their privacy, their control over what data are collected, the purpose of the collection, and the implications of any analysis. Questions arise as to the extent to which students are aware of the benefits and risks inherent in the exchange of their data, and whether they are willing to exchange personal data for more effective and supported learning experiences.

This study reports on the views of entry level students at the Open University (OU) in 2018. The primary aim is to explore differences between stated attitudes to privacy and their online behaviors, and whether these same attitudes extend to their university's uses of their (personal) data. The analysis indicates, inter alia, that there is no obvious relationship between how often students are online or their awareness of/concerns about privacy issues in online contexts and what they actually do to protect themselves. Significantly though, the findings indicate that students overwhelmingly have an inherent trust in their university to use their data appropriately and ethically.

Based on the findings, we outline a number of issues for consideration by higher education institutions, such as the need for transparency (of purpose and scope), the provision of some element of student control, and an acknowledgment of the exchange value of information in the nexus of the privacy calculus.

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