Data Management Practices in Academic Library Learning Analytics: A Critical Review

Kristin A Briney

doi:10.7710/2162-3309.2268

Abstract

INTRODUCTION Data handling in library learning analytics plays a pivotal role in protecting patron privacy, yet the landscape of data management by librarians is poorly understood. METHODS This critical review examines data-handling practices from 54 learning analytics studies in academic libraries and compares them against the NISO Consensus Principles on User’s Digital Privacy in Library, Publisher, and Software-Provider Systems and data management best practices. RESULTS A number of the published research projects demonstrate inadequate data protection practices including incomplete anonymization, prolonged data retention, collection of a broad scope of sensitive information, lack of informed consent, and sharing of patron-identified information. DISCUSSION As with researchers more generally, libraries should improve their data management practices. No studies aligned with the NISO Principles in all evaluated areas, but several studies provide specific exemplars of good practice. CONCLUSION Libraries can better protect patron privacy by improving data management practices in learning analytics research.

Highlights

Data handling in library learning analytics plays a pivotal role in protecting patron privacy, yet the landscape of data management by librarians is poorly understood
This creates a tension in that libraries espouse patron privacy (ALA, 2008; International Federation of Library Associations (IFLA), 2012) yet learning analytics fundamentally require looking at individual-level data to draw conclusions (Oakleaf et al, 2010)
Further study and transparency is merited on how libraries work through legal requirements for data, document data handling in analytics projects, secure data, and delete analytics data

Summary

Introduction

Data handling in library learning analytics plays a pivotal role in protecting patron privacy, yet the landscape of data management by librarians is poorly understood. The use of learning analytics is becoming common in response to administrative demands to demonstrate library value and improve student experience (Cullen, 2005; Oakleaf et al, 2010; Palmer, 2012; Showers & Stone, 2014; Varnum, 2015). This creates a tension in that libraries espouse patron privacy (ALA, 2008; IFLA, 2012) yet learning analytics fundamentally require looking at individual-level data to draw conclusions (Oakleaf et al, 2010). L. Jones & Salo, 2018)

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Findings

Discussion

Conclusion