Bringing in the Experts: Library Research Guide Usability Testing in a Computer Science Class
Bibliographic record
Abstract
Objective – We sought to develop best practices for creating online research guides in an academic library. Methods – We performed usability tests of particular library research guides in order to determine how to improve them. Students in a Human-Computer Interaction (HCI) class (n=20) participated in the studies both as subjects of the tests and as evaluators of the results. The students were each interviewed and then asked to review the interviews recorded of four other classmates. Based on their own experience with the guides and their viewing of their classmates using the guides, the students worked with librarians to develop best practices. Results – Students were generally unfamiliar with the library's research guides prior to the study. They identified bibliographic databases as the most important links on the guides and felt that these should be prominently placed. Opinions about many specific features (e.g., images, length of guide, annotations) varied widely, but students felt strongly that there should be some organizational consistency among the guides. Conclusions – The importance that students placed on consistency led the library to adopt guidelines dictating the inclusion of a table of contents and short list of major databases at the top of each guide, as well as uniform placement of certain other elements.
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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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".