Bibliographic record
Abstract
We present the design and evaluation of Graphically Enhanced Keyboard Accelerators (GEKA), a user interface interaction method allowing commands within a graphical application to be quickly and easily invoked through the keyboard. The high-level goal of this work is to make interactive desktop computing more pleasant and productive for experienced computer users. GEKA is designed to provide complete coverage of the command set, to require low visual demand, and to support ease of learning and remembering, a low error rate, and high speed. This thesis describes GEKA's design and two related user studies. A formative study with 10 participants explored how our target users currently work with Window, Icon, Menu and Pointer (WIMP) interfaces. The results of the study suggest that advanced computer users prefer to execute commands with the keyboard. However, they are often unable to do so in current applications because shortcuts are not available for all commands or are unknown. This indicates a desire among advanced users for a GEKA-like interaction method and motivates our research. GEKA’s design blends elements from WIMP and command line interfaces, allowing commands to be entered quickly and precisely while shifting the focus of the interaction to recognition rather than recall. GEKA has three key improvements over existing text command systems with graphical feedback: support for multiple parameters in arbitrary order, smarter matching – including abbreviations for all commands, and clear visual feedback of the input characters to facilitate learning and re-use. A laboratory experiment with 12 participants compared GEKA to WIMP interaction methods. We found error rates to be nearly identical and speed to be very competitive. The experiment also explored users’ preferences: When given a choice in situ between WIMP and GEKA for actual command execution, participants overwhelmingly used existing keyboard shortcuts when they knew them and used GEKA when they didn’t. In a questionnaire, each type of GEKA command was rated better than its WIMP equivalent except for zero-parameter GEKA commands relative to keyboard shortcuts. These results suggest that our target user population has a strong preference for GEKA interaction over the mouse-based WIMP methods.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".