A computer‐based Pinyin intervention for disadvantaged children in China: Effects on Pinyin skills, phonological awareness, and character reading
Bibliographic record
Abstract
Pinyin is an alphabetic script that denotes pronunciations of Chinese characters. Studies have shown that Pinyin instruction enhances both phonological awareness (e.g., Shu et al., Developmental Science, 2008, 11, 171-181) and character reading (e.g., Lin et al., Psychological Science, 2010, 21, 1117-1122) in Chinese children. In the present study, we provided a 3-week Pinyin intervention with a computer-based Pinyin GraphoGame to disadvantaged migrant children with poor Pinyin skills. A total of 252 first graders who were children of migrant workers in a large Chinese city were assessed to identify poor Pinyin readers. Fifty-six 7-year-old children with poor Pinyin skills were selected and randomly divided into a training group and a control group, with 28 children in each group. The training group played the Pinyin GraphoGame for 3 weeks, while the control group received school instruction only during the same period. Results showed that the children in the training group outperformed their peers in the control group on Pinyin reading accuracy and fluency, onset-rime and phonemic awareness, and character reading. These results suggest that the Pinyin GraphoGame may be a cost-effective method to enhance Pinyin and literacy outcomes for underprivileged children in China.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".