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Abstract

Higher 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.

Theme

Data Mining

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Abstract

Higher 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.