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Item A measurement of faculty views on the meaning and value of student privacy(Springer, 2022-06-04) Jones, Kyle M. L.; VanScoy, Amy; Bright, Kawanna; Harding, Alison; Vedak, Sanika; Library and Information Science, School of Computing and InformaticsLearning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. In the findings, we detail faculty perspectives of their privacy, students’ privacy, and the high degree to which they value both. Data indicate that faculty believe privacy is important to intellectual behaviors and learning, but the discussion argues that faculty make choices that put students at risk. While there seems to be a “privacy paradox,” our discussion argues that faculty are making assumptions about existing privacy protections and making instructional choices that could harm students because their “risk calculus” is underinformed. We conclude the article with recommendations to improve a faculty member’s privacy decision-making strategies and improve institutional conditions for student privacy.Item Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy(Springer, 2019) Jones, Kyle M. L.; Library and Information Science, School of Informatics and ComputingBy tracking, aggregating, and analyzing student profiles along with students’ digital and analog behaviors captured in information systems, universities are beginning to open the black box of education using learning analytics technologies. However, the increase in and usage of sensitive and personal student data present unique privacy concerns. I argue that privacy-as-control of personal information is autonomy promoting, and that students should be informed about these information flows and to what ends their institution is using them. Informed consent is one mechanism by which to accomplish these goals, but Big Data practices challenge the efficacy of this strategy. To ensure the usefulness of informed consent, I argue for the development of Platform for Privacy Preferences (P3P) technology and assert that privacy dashboards will enable student control and consent mechanisms, while providing an opportunity for institutions to justify their practices according to existing norms and values.Item A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics(Wiley, 2020) Jones, Kyle M. L.; Rubel, Alan; LeClere, Ellen; Library and Information Science, School of Informatics and ComputingHigher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.Item Reframing Student Privacy as a Common Value and Responsibility(Center for Open Science, 2021) Jones, Kyle M.; Dell, Tyler; Library and Information Science, School of Informatics and ComputingIn the American higher education context, student privacy is treated as an individual right. In this workshop paper, we argue that in light of emerging sociotechnical conditions this approach is flawed. Data mining, predictive analytics, machine learning, and artificial intelligence continue to push the boundaries of student privacy in ways once unimaginable, all of which challenge federal law, institutional policy, and contextual norms. Instead of relying on existing, non-workable conditions to protect students, we argue that institutional actors need to reframe their thinking about student data and student privacy by taking up a position that the data is a common-pool resource and privacy is a shared value—and responsibility.Item “We're being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education(Wiley, 2020) Jones, Kyle M. L.; Asher, Andrew; Goben, Abigail; Perry, Michael R.; Salo, Dorothea; Briney, Kristin A.; Robertshaw, M. Brooke; Library and Information Science, School of Informatics and ComputingHigher education institutions are continuing to develop their capacity for learning analytics (LA), which is a sociotechnical data‐mining and analytic practice. Institutions rarely inform their students about LA practices, and there exist significant privacy concerns. Without a clear student voice in the design of LA, institutions put themselves in an ethical gray area. To help fill this gap in practice and add to the growing literature on students' privacy perspectives, this study reports findings from over 100 interviews with undergraduate students at eight U.S. higher education institutions. Findings demonstrate that students lacked awareness of educational data‐mining and analytic practices, as well as the data on which they rely. Students see potential in LA, but they presented nuanced arguments about when and with whom data should be shared; they also expressed why informed consent was valuable and necessary. The study uncovered perspectives on institutional trust that were heretofore unknown, as well as what actions might violate that trust. Institutions must balance their desire to implement LA with their obligation to educate students about their analytic practices and treat them as partners in the design of analytic strategies reliant on student data in order to protect their intellectual privacy.