Introduction

Abstract

<p>The issue begins with Tami Oliphant and Michael R. Brundin's review of the learning analytics literature and role of big data in higher education, including a detailed review of how libraries are pursuing learning analytics. They home in on the stated values and ethical principles to which librarians hold strong, arguing that such things are seemingly incompatible with the aims learning analytics seek to achieve.</p> <br> <p>Respectively, the work by Sarah Hartman-Caverly and Karen P. Nicholson, Nicole Pagowsky, and Maura Seale address how temporality, manipulation, and power issues intertwined with learning analytics all act upon students and librarians in ways that diminish their autonomy and exert influence. For Hartman-Caverly, learning analytics are used to engineer student attention in ways that serve the capitalistic interests of higher education institutions. For Nicholson and her colleagues, learning analytics are a form of temporal governmentality. They argue that administrators and the politically powerful can use data analytics to scrutinize real-time performance of students and librarians, predict future outcomes (even when such outcomes are not guaranteed), and advocate for data-based interventions to resolve undesirable predicted outcomes.</p> <br> <p>M. Brooke Robertshaw and Andrew Asher's article employs a statistically driven meta-analysis strategy of library learning analytics research. The analyzed corpus included common "library value" research that suggests a positive correlation between grade-point average and library usage or library instruction. Robertshaw and Asher found, however, that the effect of such findings is statistically weak, and they argue that the potential privacy harms are not justified by the analytic practices.</p>