Investigating the correlation of usability measures and user tests : a roadmap for a predictive model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it