The effects of dynamic dwell time systems on the usability of eye-tracking technology: a systematic review and meta-analyses
Bibliographic record
Abstract
Eye-tracking can be used by individuals with severe motor impairments as a hands-free access method to computer systems. The traditional eye-tracking selection method employs static dwell times that are held constant throughout a task. The value of this parameter has an inherent speed-accuracy trade-off, with long dwell times being accurate but slow, and short dwell times being efficient but error-prone. To address this usability flaw, emerging research has introduced dynamic dwell time systems that employ either the user or a computer algorithm to vary the dwell time parameter throughout the task. A systematic review was undertaken to determine the modes and mechanisms of dwell time manipulation employed by these dynamic systems and the computer-based tasks they support. From six academic databases, 2765 relevant studies were identified. After removal of duplicates, title and abstract screening, and full text screening, four journal articles and eight conference proceedings evaluated dynamic dwell time systems. A series of fixed elements meta-analyses showed that the dynamic dwell time systems were significantly more usable than their static dwell time comparators. Dynamic systems may mitigate the speed-accuracy trade-offs associated with static dwell times. Future research required to further increase the usability of these systems is discussed.
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.
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 | 0.024 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".