Evaluating the Benefits of Displaying Word Prediction Lists on a Personal Digital Assistant at the Keyboard Level
Bibliographic record
Abstract
Visual-cognitive loads influence the effectiveness of word prediction technology. Adjusting parameters of word prediction programs can lessen visual-cognitive loads. This study evaluated the benefits of WordQ word prediction software for users' performance when the prediction window was moved to a personal digital assistant (PDA) device placed at the keyboard level. Twenty-one young people aged 11-14 (11 new users and 10 experienced users) participated in the study. The Canadian Occupational Performance Measure was used to measure users' self-ratings of performance and satisfaction. Results of two-tailed paired t-tests reveal significantly (p < .05) higher performance and satisfaction ratings when the word prediction list was displayed on the PDA. Users reported that it was easier to look for the words at the keyboard level and to select words directly from the PDA. Visual comparisons of the users' typing speed and accuracy show that experienced users had faster typing rates and new users appeared to have better accuracy when they typed with WordQon the PDA display. Further studies with larger samples of individuals with different diagnoses and ages are required to confirm the benefit of using a PDA display in enhancing typing rate and accuracy.
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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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".