Posts tagged with qualitative

The findings from a qualitative study on users of open access (OA) books reveal a wide range of needs and impacts. The data comes from two collections: one of OA books published by Lever Press (https://www.leverpress.org/) and the other of backlist books turned OA by the Big Ten Open Books project (https://bigtenopenbooks.org/). While the samples are small, the findings highlight the importance of qualitative approaches to capturing the variety of reader and, more broadly, user experiences.

This blog post reflects on the learning experience I had as a novice user research (UX) intern at the U-M Library. Through this nurturing and eye-opening experience, I enhanced my understanding of research operation, the activities which support the user research conducted by library employees in the Design & Discovery unit of the library.

We know very little about how authors and readers experience the impact of open-access (OA) books. Usage metrics and citations obscure their humanity. In Fall 2023, we interviewed authors and readers of monographs published as OA by the University of Michigan Press. Our qualitative research project documented their experiences, used AI to discover patterns in their responses, and provided evidence-based recommendations for improving OA book publishing.

The U-M Library UX + Design Team conducted a benchmark survey in late 2022 aiming to understand people’s experience with Library Search. Objectives included measuring user satisfaction, identifying audience needs, and comparing results with Harvard University’s survey using the same methodology. Survey findings guided the development of the Library Search Product Statement as well as user centered improvements such as implementing LibKey Direct-to-PDF API, and refining catalog results filters.

In Fall 2022, the Library Environments department began a pilot of two designated “zoned” spaces in response to user feedback asking for more information about what to expect from a study space. We conducted focus groups and integrated participatory design to learn about how users are perceiving and experiencing these labeled spaces.

In this interview, Dr. Wilkinson Daniel Wong Gonzales (U-M alum; PhD in Linguistics 2022) describes why he decided to share the data set entitled "The Lannang Corpus (LanCorp): A POS-tagged, sociolinguistic corpus containing recordings and transcriptions of Lannang speech collected from the metropolitan Manila Lannangs between 2016 and 2020" in Deep Blue Data.

The University of Michigan Library’s efforts to develop our digital preservation program created an opportunity to request additional support during the annual budget cycle. With only a few months to draft recommendations, the Digital Preservation Steering Committee performed an assessment survey to gather feedback from stakeholders across the library.

A redesign of the library blogs platform kicked off last fall with time dedicated to understanding the current site and its usage, reviewing what other libraries do, and conducting a needs assessment survey with stakeholders. This approach has allowed efficient decision making and informed requirements, while engaging stakeholders early in the redesign process.

The University of Michigan Library is home to a vast collection of materials representing dozens of languages. U-M Library Catalog Search, however, can cause difficulties for users searching for materials in languages other than English. In Summer 2021 we conducted an exploratory study on the experience of searching for non-English materials within U-M Library Catalog Search in order to better understand challenges users face, how they overcome them, and what we can do to mitigate the problem.

When you use library services, do you think about the interaction-generated data? The U-M Library collects data on its patrons, from user profiles to online resource access information. Recently, the library has considered using this data to engage in library analytics, making inferences about users’ future behaviors. An Engagement Fellows project that began in 2020 seeks to learn more about what library patrons think of the use of analytics at the U-M Library.