A field evaluation of an adaptable two-interface design for feature-rich software
Bibliographic record
Abstract
Two approaches for supporting personalization in complex software are system-controlled adaptive menus and user-controlled adaptable menus. We evaluate a novel interface design for feature-rich productivity software based on adaptable menus. The design allows the user to easily customize a personalized interface, and also supports quick access to the default interface with all of the standard features. This design was prototyped as a front-end to a commercial word processor. A field experiment investigated users' personalizing behavior and tested the effects of different interface designs on users' satisfaction and their perceived ability to navigate, control, and learn the software. There were two conditions: a commercial word processor with adaptive menus and our prototype with adaptable menus for the same word processor. Our evaluation shows: (1) when provided with a flexible, easy-to-use and easy-to-understand customization mechanism, the majority of users do effectively personalize their interface; and (2) user-controlled interface adaptation with our adaptable menus results in better navigation and learnability, and allows for the adoption of different personalization strategies, as compared to a particular system-controlled adaptive menu system that implements a single strategy. We report qualitative data obtained from interviews and questionnaires with participants in the evaluation in addition to quantitative data.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".