Bibliographic record
Abstract
The research in this dissertation developed and evaluated a new method for menuing interaction that is intended to be better suited than current methods with respect to mobile eyes-free scenarios. The earPod prototype was developed and then evaluated in a series of four experiments. In the first two experiments, earPod was first compared against an iPod-like (visual) interface and then against a fuller set of competitive techniques that included dual vs. single modality presentations, audio vs. visual modalities, and radial vs. linear mappings. The third experiment consisted of a longitudinal study designed to understand the learning patterns that occurred with these techniques. The fourth experiment examined performance in a conventional (single task) desktop setting and in a driving simulator (i.e., a dual task situation where participants carried out the driving task while interacting with the mobile device). The results of these experiments, comparing earPod with an iPod-like visual linear menu technique on fixed-sized static menus, indicated that earPod is comparable both in terms of speed and accuracy. Thus it seems likely that earPod should be an effective and efficient eyes-free menu selection technique. The comprehensive 3x2 study implemented in Experiment 2 showed that the benefit of earPod was largely due to the radial menu style design. While performance using it was comparable in both speed and accuracy with the visual linear menus, its performance was slower than for a visual radial style menu. In the multi-task simulated driving condition in Experiment 4, where concurrent tasks competed for visual attention, the eyes-free earPod interface was found to be beneficial in improving performance with respect to the safety related driving parameters of following distance and lateral movement in the lane. Thus auditory feedback appears to mitigate some of the risk associated with menu selection while driving. Overall, the results indicated that not only should earPod menuing be able to provide safer interaction in dual task settings, but also that, with sufficient training, audio only menu selection using innovative techniques such as those employed by earPod can be competitive with visual menuing systems even in desktop settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".