Letting Students Take the Lead: A User-Centred Approach to Evaluating Subject Guides
Bibliographic record
Abstract
Objective – What do students need and want from library subject guides? Options such as Web 2.0 enhancement are now available to librarians creating subject-specific web pages. Librarians may be eager to implement these new tools, but are such add-ons a priority for students? This paper aims to start a dialogue on this issue by presenting the findings of the University of British Columbia (UBC) Library’s Subject Guides Working Group (SGWG), which was tasked with assessing current library subject guides in order to make recommendations for the update and future development of UBC Library subject guides. Methods – The working group solicited feedback through a questionnaire that was distributed to undergraduate and graduate students from a variety of disciplines at UBC. The questionnaire included an evaluation of UBC subject guides, as well as guides from other academic libraries that used various platforms such as LibGuides and SubjectsPlus. Results – Respondents to the student questionnaire indicated that a simple and clean layout was of primary importance. Students also desired succinct annotations to resources and limited page scrolling. Meanwhile, few students identified Web 2.0 features such as rating systems and discussion forums as being important for their needs. The working group used the questionnaire data to create a “Top Ten” list of student recommendations. Conclusions – The “Top Ten” list of student recommendations was combined with stakeholder feedback from faculty, liaison librarians and Library Systems and Information Technology representatives to create the SGWG’s final recommendation for subject guide revision and enhancement. For the SGWG these findings called into question the necessity of Web 2.0 technologies within subject guide pages and highlighted the need for further research on the topic of subject guide usability and effectiveness.
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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 | 0.104 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".