Interacting with big interfaces on small screens: a comparison of fisheye, zoom, and panning techniques
Bibliographic record
Abstract
Mobile devices with small screens are becoming more common, and will soon be powerful enough to run desktop software. However, the large interfaces of desktop applications do not fit on the small screens. Although there are ways to redesign a UI to fit a smaller area, there are many cases where the only solution is to navigate the large UI with the small screen. The best way to do this, however, is not known. We compared three techniques for using large interfaces on small screens: a panning system similar to what is in current use, a two-level zoom system, and a fisheye view. We tested the techniques with three realistic tasks. We found that people were able to carry out a web navigation task significantly faster with the fisheye view, that the two-level zoom was significantly better for a monitoring task, and that people were slowest with the panning system. ways to solve this problem. First, applications can be redesigned for the smaller screen. Although there are examples of this approach (e.g., Pocket Word or Internet Explorer for PocketPC devices), it requires that multiple versions of the application be produced, and requires that users become familiar with a second GUI. There will also be cases where no redesigned version of an application is available—so another approach is needed. Key words: Large interfaces, small screens, mobile devices, screen space, zoom and pan, fisheye views. 1
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".