The Impact of Word Prediction Software on the Written Output of Students with Physical Disabilities
Bibliographic record
Abstract
This study examined the impact of a word prediction software program, Co:Writer, on the written output of 24 students with physical disabilities that affected their ability to write by hand. Surveys were completed by both students who used Co:Writer and their teachers/adult supporters in schools, and 10-minute writing samples were obtained from students in three modalities: handwriting, word processing, and word processing with Co:Writer. Two-thirds or more of the students and 50% or more of the adults believed that Co:Writer helped the students to spell better; use a wider variety of words; write faster; produce neater, easier-to-read work; and write more correct sentences. Further, two-thirds or more of the adults and 50% or more of the students believed that Co:Writer helped the students to write more without tiring, experience less frustration when writing, and read what they had written. The writing sample analyses indicated no significant difference between the three writing modes with regard to the total number of words produced in 10 minutes. However, word processing and/or Co:Writer resulted in higher percentages of legible words, correctly spelled words, and correct word sequences; and in longer mean lengths of consecutive correct word sequences than handwriting. The results are discussed in terms of their relevance to educational technology supports for students with physical disabilities.
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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 | 0.022 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".