Unpacking Privacy in Learning Analytics for Improved Learning at Scale

TheSociety for Learning Analytics Research (SoLAR)is an inter-disciplinary network of leading international researchers who are exploring the role and impact of analytics on teaching, learning, training and development. SoLAR has been active in organizing theInternational Conference on Learning Analytics & Knowledge (LAK)and theLearning Analytics Summer Institute (LASI), launchingmultiple initiativesto support collaborative and open research around learning analytics, promoting the publication and dissemination of learning analytics research, and advising and consulting with state, provincial, and national governments. Learn more about SoLAR athttps://solaresearch.org/

Society for Learning Analytics Research (SoLAR)

This SoLAR webinar will focus on the findings of two studies about privacy from the perspective of LA scholars and students.

AbstractAddressing student privacy is critical for large-scale learning analytics implementation. Defining and understanding what privacy means as well as understanding the nature of students’ privacy concerns are two critical steps to developing effective privacy-enhancing practices in learning analytics. This SoLAR webinar will focus on the findings from two studies. First, we will present our preliminary results on how privacy has been defined by LA scholars, and how those definitions relate to the general literature on privacy. Second, a validated model that explores the nature of students’ privacy concerns (SPICE) in learning analytics in the setting of higher education will be presented. 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. Partial Least Square results show that the model accounts for high variance in privacy concerns, trusting beliefs, and non-self-disclosure behaviours. They also demonstrate that students’ perceived privacy risk is a firm predictor of their privacy concerns. Further, a set of relevant implications for LA designers will be offered. Finally, we will present future research directions, with a focus on a value-based approach to privacy in learning analytics.BioOlga Viberg is associate professor in Media Technology, with specialization in technology-enhanced learning at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Sweden. Her current research includes a focus on responsible use of student data in education, self-regulated learning, value-sensitive design of learning analytics’ services, mobile learning and cross-cultural research. Viberg is one of the key coordinators of the Responsible Learning Analytics SIG, and she is one of the key organizers of the Nordic Learning Analytics Summer Institute in 2021 and 2022. Further, she has served as the keynote speaker at several international conferences and has contributed to UNESCO's policy work on quality of online education.
2pm 21 June 2022 CEST (<a href="https://www.timeanddate.com/worldclock/fixedtime.html?iso=20220427T2300" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">convert</a>)

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