Abstract
With the rise of digital systems in learning scenarios in recent years as learning management systems, massive open online courses, serious games, and the use of sensors and IoT devices huge amounts of personal data are generated. In the context of learning analytics, this data is used to individualize contents and exercises, predict success or dropout. Based on a meta analysis it is investigated to which extent the privacy of learners is respected. Our research found that, although surveys have shown that privacy is a concern for learners and critical to adopt to establish trust in learning analytic solutions, privacy issues are very rarely addressed in actual learning analytic setups.
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Acknowledgement
This work was supported by the Leibniz Association and the Ministry for Science and Culture of Lower Saxony as part of Leibniz ScienceCampus – Postdigital Participation – Braunschweig.
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Priedigkeit, M., Weich, A., Schiering, I. (2021). Learning Analytics and Privacy—Respecting Privacy in Digital Learning Scenarios. In: Friedewald, M., Schiffner, S., Krenn, S. (eds) Privacy and Identity Management. Privacy and Identity 2020. IFIP Advances in Information and Communication Technology, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-030-72465-8_8
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