TapFix: Cursorless Typographical Error Correction for Touch-Sensor Displays
Bibliographic record
Abstract
We present TapFix, a cursorless mobile text correction method for touch-sensor displays. Unlike traditional methods, TapFix eliminates the need to position a cursor to make corrections. Instead, the method allows for direct, character-level access, offering easy-to-use swipe gestures on a zoomed-in target word for corrective actions. A user study with 15 participants compared TapFix to two traditional text correction methods on Apple iOS. For each of the three methods, participants completed 100 text correction tasks of four different error types on an Apple iPhone 14 Pro. The TapFix method was on average between 43.0% and 44.1% faster than the existing methods in completing the tasks. Participants also reported experiencing 5.6% to 21.1% lower levels of frustration with TapFix, as indicated by post-experiment NASA TLX and SUS questionnaires, compared to the traditional methods. Additionally, they attributed a level of usability to TapFix that was comparable to the well-established TextMagnifier method.
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 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 | 0.001 |
| 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".