"Employing Analytics Data to Guide a Data-Driven Review of LibGuides" by Melanie Griffin

Article

Employing Analytics Data to Guide a Data-Driven Review of LibGuides

Journal of Web Librarianship (2018)

Melanie Griffin, University of South Florida
Tomaro I. Taylor

Abstract

This article presents a methodology for conducting an evidence-based review of LibGuides content based on native and non-native analytics data. This methodology uses built-in analytics data from Springshare's platform and data from Google Analytics to investigate LibGuides functionality, use, and design criteria. These criteria, in turn, enable a strategic consideration of how and why we as librarians create LibGuides. Are our guides intended to facilitate reference and research consultations, or do they primarily serve to enable independent research by students? More specifically, who benefits the most from the LibGuides we generate—librarians or researchers? We conclude with a consideration of how analytics data can be leveraged to generate librarian buy-in for reevaluating design criteria of library subject guides and consider implications for practice and further research in this area.

Keywords

LibGuides,
Google Analytics,
user behavior,
assessment,
analytics data,
library guides

Disciplines

Library and Information Science

Publication Date

June, 2018

DOI

10.1080/19322909.2018.1487191

Citation Information

Melanie Griffin and Tomaro I. Taylor. "Employing Analytics Data to Guide a Data-Driven Review of LibGuides" Journal of Web Librarianship (2018)
Available at: http://works.bepress.com/ttaylor/40/