Enhancing Spatial Learning of Large Command Sets in Two FastTap Menus with Artificial Landmarks
Bibliographic record
Abstract
Spatially-stable menus, FastTap, use grids to display commands on touch tablets, where grid lines serve as landmarks, enabling users to quickly learn command locations. While these menus can expand the grid to allow more commands, repetitive lines can weaken landmark aid and hinder spatial learning. While artificial landmarks showed spatial learning benefits in desktop menus, little is known if landmarks can enable spatial learning on tablets with many commands. So, we conducted two studies with different landmarks (standard grid, menu-backdrop image, and coloured blocks) in single-tab and multi-tab FastTap menus with large command sets. Results indicated that people developed spatial memory of commands in all interfaces across two studies. While coloured blocks substantially improved command selection performance in single-tab, both landmarks notably improved selection speed at least during the early learning stages in multi-tab. People also issued significantly more expert selections with landmarks in multi-tab. We demonstrate that artificial landmarks can aid spatial learning in tablets with large command sets, which will help the future design of tablets.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".