Bringing in the Experts: Library Research Guide Usability Testing in a Computer Science Class
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.451 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it