We are proud to announce that Dr. Kyle Jones’ work with Lisa Janicke Hinchliffe was just announced as the winner of the “ALISE Best Conference Paper Award” for the upcoming annual conference. Congratulations are also due to Dr. Jones and his co-authors, Alan Rubel and Ellen LeClere, for an honorable mention for their forthcoming paper in the Journal of the Association for Information Science and Technology.
The ALISE paper, which is entitled “New methods, new needs: Preparing academic library practitioners to address ethical issues associated with learning analytics,” reflects emerging results from their survey for their grant-funded Prioritizing Privacy project. The abstract follows:
Academic libraries are participating in the collection and analysis of student data. Under the umbrella of learning analytics, these practices are directed toward developing an understanding of how libraries contribute to student learning, the educational experience, and efficient operations of academic institutions. Learning analytics, however, is loaded with ethical issues, which are complicated by privacy-related values espoused by library practitioners. This work-in-progress paper discusses emerging findings from a survey of academic library practitioners. The survey identifies what ethical issues practitioners associate with leaning analytics and the degree to which they are prepared to address such issues.
The Association for Information Science and Technology’s (ASIS&T) Special Interest Group for Social Informatics (SIG SI) recently announced its annual winners. Dr. Jones and his collaborators’ paper, “A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics,” received one of the two honorable mentions. The paper’s abstract follows:
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.