StencilMaps and EphemeralMaps: spatially stable interfaces that highlight command subsets
Bibliographic record
Abstract
Identifying a target command can be difficult and time-consuming when the user is unfamiliar with a software system. One technique for assisting command identification is to provide a subset interface that contains only a limited set of the system's capabilities. We examine the design of subset interfaces, showing that subsets can be presented separately to the full user interface (UI) (e.g. in a palette) or in place, with in-place methods using either static or dynamic methods to identify the subset. We introduce the StencilMap and EphemeralMap as in-place subset UIs that, respectively, use static and dynamic highlighting. Both StencilMaps and EphemeralMaps make all of an application's commands concurrently available for selection within a grid. To highlight subset items StencilMaps use a static dark semi-transparent ‘stencil’ overlay to de-emphasise all but the subset items; EphemeralMaps, in contrast, use a short delay, with subset items shown immediately, and other items gradually faded in. A first experiment compares user performance with the in-place presentation of StencilMaps against that of the separate presentation of a subset palette. Results confirm the predicted spatial memory benefits for StencilMaps. A second experiment analyses the performance impact of three approaches to highlighting: none, static highlighting in StencilMaps, and dynamic highlighting in EphemeralMaps. Results show an interesting trade-off – while highlighting can offer benefits in assisting rapid target identification (particularly when the user is unfamiliar with the interface layout), there can also be longer-term performance benefits when highlighting is absent because the increased difficulty of visual search promotes the use and formation of spatial memory.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".