Investigating the correlation of usability measures and user tests : a roadmap for a predictive model
Bibliographic record
Abstract
Most of the existing usability evaluation and testing methods require a fully functional prototype. As a consequence, tests are conducted after the development and most often, after the deployment of the whole software. Furthermore, tests require a costly usability laboratory and highly trained usability testers, usually developers lack training in conducting such tests. Cost-benefit studies show that these problems and similar ones result in significant costs. Predictive usability models have been introduced as potential solutions to address these crucial drawbacks of the existing usability evaluation methods. Predictive usability models and measures can supplement the existing evaluation methods while reducing costs and enhancing efficiency, accuracy and objectiveness of the tests. In this thesis, we demonstrated via empirical investigations, that usability measures and user-oriented tests conducted by users can provide similar scores regarding the overall usability as well as two core usability parameters: Learnability and Efficiency. Moreover this thesis demonstrated that the results of empirical investigations can be used to build measure-based models for usability prediction. As an outcome, this thesis introduced a comprehensive methodology to develop and validate measure-based models for usability prediction from early user interface design artifacts including storyboards and prototypes. This methodology includes a systematic process that involves discovery of correlations between usability measures and the results of usability tests performed by users.
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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 | 0.154 | 0.469 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".