Usability and Frustration in Using Adaptive Functions for Decision Making: A User Study
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
Information Systems (IS) are vital for a wide range of applications, particularly those that prioritize safety and rely heavily on them for their effectiveness and reliability. While several advantages are associated with them, there is an increasing apprehension regarding their safety consequences. Despite extensive research and publication on the safety advantages of IS, several case studies have shown distinct, potentially life-threatening problems and safety dangers associated with IS. Accidents occur when there is a disparity between the user’s perception of the situation and the actual state of the regulated or managed process. This is remarkably accurate for accidents involving user interactions and safety information systems. To mitigate this issue, this research suggests implementing a Self-Adaptive System (SAS). This advanced technology has the potential to autonomously adapt to dynamic events and environments, thereby significantly enhancing usability and reducing user frustration. Our study, which utilized SAS software, aimed to assess the influence of user usability and frustration. In this design, the IS automatically adjusts its information content and navigation structure based on the circumstances of its use, such as compensating for user distraction. To verify the validity of our findings, we conducted a controlled experiment. The investigation aimed to determine whether an SAS, which possesses knowledge of interruptions, enhances usability and reduces frustration levels compared to non-adaptive systems (NAS). The controlled trial results indicated that individuals using the SAS exhibited better usability than those using the NAS and experienced significantly less frustration than a specific type of NAS.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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