A Framework for Measuring Relevancy in Discovery Environments: Increasing Scalability and Reproducibility.

Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process with a fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments. [ABSTRACT FROM AUTHOR]