Usability and user perceptions of a thesaurus‐enhanced search interface
Bibliographic record
Abstract
Purpose This paper seeks to report an investigation into the ways in which end‐users perceive a thesaurus‐enhanced search interface, in particular thesaurus and search interface usability. Design/methodology/approach Thirty academic users, split between staff and postgraduate students, carrying out real search requests were observed during this study. Users were asked to comment on a range of thesaurus and interface characteristics including: ease of use, ease of learning, ease of browsing and navigation, problems and difficulties encountered while interacting with the system, and the effect of browsing on search term selection. Findings The results suggest that interface usability is a factor affecting thesaurus browsing/navigation and other information‐searching behaviours. Academic staff viewed the function of a thesaurus as being useful for narrowing down a search and providing alternative search terms, while postgraduates stressed the role of the thesaurus for broadening searches and providing new terms. Originality/value The paper provides an insight into the ways in which end‐users make use of and interact with a thesaurus‐enhanced search interface. This area is new since previous research has particularly focused on how professional searchers and librarians make use of thesauri and thesaurus‐enhanced search interfaces. The research reported here suggests that end‐users with varying levels of domain knowledge are able to use thesauri that are integrated into search interfaces. It also provides design implications for search interface developers as well as information professionals who are involved in teaching online searching.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".