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Unethical Numbers? A Meta-analysis of Library Learning Analytics Studies

LIBRARY TRENDS, Vol. 68, No. 1, 2019 (“Learning Analytics and the Academic Library: Critical Questions about Real and Possible Futures,” edited by Kyle M. L. Jones), pp. 76–101. © 2019 The Board of Trustees, University of Illinois Abstract Following trends in higher education that emphasize quantitative analytical approaches to assess educational outcomes, academic libraries are increasingly attempting to quantify their impacts on student learning and demonstrate their value to the university’s educational mission. By applying learning analytics techniques to library use and instructional data, libraries have especially focused on attempting to measure the impact of the library on student GPA, retention, and attainment measures. Because learning analytics studies typically require large datasets of personally identifiable information (PII), they present inherent risks to the privacy, confidentiality, and autonomy of research subjects, who often are unaware and uninformed of the data collected. This paper presents the results of a meta-analysis of learning analytics studies in libraries that examine the effects of library use on measures of student success. Based on the aggregate results, we argue that outcomes of these studies have not produced findings that justify the loss of privacy and risk borne by students. Moreover, we argue that basing high-impact decisions on studies with no, or low, effect sizes, and weak correlation or regression values, has the potential to harm students, particularly those in already vulnerable populations. Finally, we believe that these studies also have the potential to harm institutions that rely on these particular analytical approaches to make crucial business and educational decisions.