Towards an Integrated Evaluation of Usability, User Experience and Accessibility in Assistive Technologies
Bibliographic record
Abstract
The development of Assistive Technologies (AT) has been growing and promoting the autonomy of their users, aiming at quality of life, accessibility, and inclusion. In this way, one of the main quality factors is the Usability, that aims at the user having a good efficiency, effectiveness, and satisfaction when using the technology. In order to improve the ATs usability, it is necessary to go through the usability evaluation. In this paper, we present a usability evaluation carried out with an AT using two different techniques, QUEST (Quebec User Evaluation of Satisfaction with Assistive Technology) and SUS (System Usability Scale). In this evaluation, regarding usability, we focused on the aspect of user satisfaction. However, we perceived that we omitted some aspects that are relevant to consider in an AT. We also observed that analyzing only the Usability concept will leave gaps in the quality criterion. This happens because an AT needs to be accessible and bring good experiences to the user. Therefore, we recommended that to meet most of the requirements of the user and the AT, there must be an evaluation that involves the concepts of usability, accessibility, and user experience together.
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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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".