Multi-modal text entry and selection on a mobile device
Bibliographic record
Abstract
Rich text tasks are increasingly common on mobile devices, requiring the user to interleave typing and selection to produce the text and formatting she desires. However, mobile devices are a rich input space where input does not need to be limited to a keyboard and touch. In this paper, we present two complementary studies evaluating four different input modalities to perform selection in support of text entry on a mobile device. The modalities are: screen touch (Touch), device tilt (Tilt), voice recognition (Speech), and foot tap (Foot). The results show that Tilt is the fastest method for making a selection, but that Touch allows for the highest overall text throughput. The Tilt and Foot methods—although fast—resulted in users performing and subsequently correcting a high number of text entry errors, whereas the number of errors for Touch is significantly lower. Users experienced significant difficulty when using Tilt and Foot in coordinating the format selections in parallel with the text entry. This difficulty resulted in more errors and therefore lower text throughput. Touching the screen to perform a selection is slower than tilting the device or tapping the foot, but the action of moving the fingers off the keyboard to make a selection ensured high precision when interleaving selection and text entry. Additionally, mobile devices offer a breadth of promising rich input methods that need to be careful studied in situ when deciding if each is appropriate to support a given task; it is not sufficient to study the modalities independent of a natural task.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".