LibGuides or Bust? Usability Testing Platforms for Research Guides
Bibliographic record
Abstract
Objective – The purpose of this study was to compare student use of two library research guides, one created in Springshare’s LibGuides platform with a multi-page layout and one created using a single-page Adobe Express website. The researchers sought to explore how LibGuides’ library-first design and embedded features would enable successful student navigation versus Adobe Express’ simpler, website-style layout that might be more familiar to users yet lacks the ability to integrate fully with the broader library Web presence. Methods – The study used a qualitative usability test to answer the research questions. Two groups of six users each were assigned to complete tasks using one of the two platforms. Researchers designed test guides for the study mirroring real-world content and layout, following best practices of design and institutional guidelines within each system. During test sessions, users followed a “think-aloud” protocol, allowing researchers to transcribe and code comments to identify patterns in students’ feedback. Pretest and posttest questionnaires were also used to assess participants’ prior experience and subsequent perceptions. Results – Users’ posttest responses indicated they found both guides easy to use. However, there were a few differences between their use of LibGuides and Adobe Express during the sessions. Users of Adobe Express commented favorably on its clean aesthetic, though users appreciated both guides’ collation of resources. Adobe Express users experienced higher rates of task success, fewer instances of confusion, and a clearer differentiation between the guide and broader library resources. Many themes that surfaced in sessions related to user behaviour overlapped between the two platforms, such as a preference for searching over browsing, gravitation toward familiar tools, and not reading all content. Conclusion – Neither guide fully enabled universal task success, and each brought its own set of challenges for users. Moreover, users in both test groups failed to fully engage with all content. Librarians designing research guides should consider the context and purpose of creation, as well as users’ existing information literacy skills and mental models, when selecting platforms, layouts, and designs.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".