IDEA: Index of Difficulty for Eye Tracking Applications - An Analysis Model for Target Selection Tasks
Bibliographic record
Abstract
Fitts' law is a prediction model to measure the difficulty level of target selection for pointing devices. However, emerging devices and interaction techniques require more flexible parameters to adopt the original Fitts' law to new circumstances and case scenarios. We propose Index of Difficulty for Eye tracking Applications (IDEA) which integrates Fitts' law with users' feedback from the NASA TLX to measure the difficulty of target selection. The COVID-19 pandemic has shown the necessity of contact-free interactions on public and shared devices, thus in this work, we aim to propose a model for evaluating contact-free interaction techniques, which can accurately measure the difficulty of eye tracking applications and can be adapted to children, users with disabilities, and elderly without requiring the acquisition of physiological sensory data. We tested the IDEA model using data from a three-part user study with 33 participants that compared two eye tracking selection techniques, dwell-time, and a multi-modal eye tracking technique using voice commands.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".