Bibliographic record
Abstract
Dyslexia is a severe impairment in reading and spelling that affects 10% of children in English-speaking countries. One area of difficulty is learning spelling rules that require attention to other letters within a word (i.e., context): for example, why grapple requires two ps while staple requires one. Poor visual attention contributes to children's difficulties. Computer-based programs that use multisensory cues have helped children learn simple letter-sound relations, but not contextual spelling rules. In this paper we present three theoretically derived principles that can be used to design dynamic colour codes for a variety of contextual spelling rules in software systems. We discuss how we used our principles to design the colour scheme for a single contextual spelling rule in our tangible software system, called PhonoBlocks. We evaluate its effectiveness in a field study with nine dyslexic children. On the basis of our findings, we conclude that our approach to using dynamic colour may help children with dyslexia to learn contextual spelling rules, but that individual factors impact the colours' effectiveness. We conclude by suggesting ways our dynamic colour-coding principles can be implemented in other systems taking into consideration individual factors that also impact their effectiveness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".