Evaluation of Different Teaching Strategies in Sight Words Instruction of Chinese Children with Autism: Text-picture Matching, Picture-embedded, and Word Tracing
Bibliographic record
Abstract
This study investigated and compared whether text-picture matching, picture-embedded and word tracing intervention strategies can promote the learning of Chinese characters by Chinese ASD children. The core theory is the effect of picture stimulation and handwriting stimulation on literacy ability. The participants were all 24 children aged 6-12 years in a rehabilitation center in Shanxi Province, China. The children were randomly divided into a treatment group and a control group; the treatment group used these three methods to teach four Chinese characters in turn, and the control group used text-only. A total of three intervention experiments were conducted, and each experimental group used different intervention methods to teach new Chinese characters. The results show that these three intervention strategies all promote the literacy of Chinese children with ASD, but the picture-embedded teaching strategy is not recommended. Due to the short experimental period, future work can continue to study and compare the effectiveness of text-picture matching and word tracing.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".