User Experience Research Techniques Facilitate Improvements for Access and Discovery Tools Managed by Technical Services Librarians
Bibliographic record
Abstract
A Review of: Hill, K. (2020). Usability beyond the home page: Bringing usability into the technical services workflow. The Serials Librarian, 78 (1–4), 173–180. https://doi.org/10.1080/0361526X.2020.1702857 Objective – To demonstrate how user experience research techniques can be incorporated into technical services work. As proof of this concept, the author describes a case wherein a team of librarians, including one in a technical services role, deployed a user experience study to determine if students were able to successfully use LibGuides and the A-Z Database List to find subject-specific resources. The study also aimed to gauge the potential for several A-Z Database List interface redesign options. Design – A case study of user experience techniques applied to technical services projects, including a classic usability test of existing tools and an A/B/C comparison of potential interface redesigns. Setting – The library at the University of North Carolina Greensboro (UNCG), a public R2 university (doctoral university with high research activity). Subjects – Eleven student participants recruited through convenience sampling. Methods – The research team recruited study participants who were in the library at the time of the study, deselecting students from UNCG’s library school and those who were not currently affiliated with the university through an initial questionnaire. Eleven student participants were ultimately selected and led through a series of tasks related to finding subject-specific databases using the A-Z Database List and LibGuides. After the tasks for the A-Z Database List were completed, students were asked for their impression of two additional database list interfaces. Students were recorded throughout the tasks using the “talk aloud” method to provide researchers with insights on their thought processes and preferences. Following the study, researchers listened to the recordings, coding them as successful or incomplete and noting their observations for use in generalized findings. Main Results – Eight of eleven participants used the library’s main search box to locate a general resource for their major on the library’s homepage. When shown the A-Z Database List, ten out of eleven participants used the list to find a database for their major, while one used the link to “Research guides by subject” from that page. Comparisons of three A-Z Database List interfaces showed that most students preferred the Springshare Content Management System that allowed for filtering by subject area. When asked to find a research guide for their subject or major from the library’s homepage, nine out of eleven students clicked on the link labeled “Research guides by subject.” Starting from their subject guide, ten out of eleven could find a tab listing article databases. Nine participants noted that the number of databases listed on the guides was daunting. Conclusion – Results from the user experience study were used to support a redesign of the A-Z Database List using the Springshare Content Management System. The author regarded the experience as a whole as demonstrating how technical services librarians can become involved in user experience work and incorporate findings from usability studies into their management and design of tools that promote access and discoverability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".