Invented spelling: what is the best way to improve literacy skills in kindergarten?
Bibliographic record
Abstract
We examined the progress made by 132 six-year-old French-speaking children in their preliteracy skills during four kinds of interventions. Three of these interventions concerned invented spelling, where the children tried to spell words. In the first condition, they were encouraged to reflect on conventional spellings. In the second condition, they reflected on spellings that were slightly more complex than theirs, while in the third condition, they reflected on increasingly complex spellings that eventually led to the conventional spellings. The fourth condition (control) consisted of phonological training. We assessed the children’s phonological awareness, letter knowledge, spelling, and decoding skills, controlling for vocabulary and nonverbal cognitive ability. Posttest results indicated progress in each condition. The greatest progress was observed in the second condition for decoding, spelling, letter-name knowledge and syllable awareness, and in the control condition for phoneme awareness. Overall, results showed that all kinds of interventions led to very similar levels of progress, but that improvements were greater for interventions that focused on the children’s initial invented spellings - in other words, when they adopted a Vygotskian perspective.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".