Effects of word prediction and location of word prediction list on text entry with children with spina bifida and hydrocephalus
Bibliographic record
Abstract
In this study, a single-subject alternating-treatments design was used to evaluate the effect of word prediction on the rate and accuracy of text entry and to compare the effect of location of a word prediction list on the rate and accuracy of text entry. Three locations were evaluated: upper right corner, following the cursor, and lower middle border. KeyREP© was the word prediction software used in this study. Three girls and one boy aged 10 to 12 years with spina bifida and hydrocephalus participated in the study over a period of 20 days. The rates and accuracy of text entry were measured on a copy-writing task. It was found that word prediction did not improve the rates of text entry but did improve the accuracy of text entry when the prediction list was placed in the lower middle border. Statistically, there was no difference in rate or accuracy when the prediction list was placed in different locations; however, three participants recorded the lowest rate, and all participants achieved lowest accuracy when the prediction list followed the cursor. The findings are discussed in terms of user characteristics, the dictionary used in the software, and the nature of the writing task (copying text) because these are common factors that can affect the effectiveness of word prediction.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".