Using Dynamic Assessment to Measure Morpheme Identification and Predict Character Reading Among Chinese Children
Bibliographic record
Abstract
Abstract In the present study, we designed a dynamic measure to assess emerging morphological awareness in Chinese children and examined its concurrent and longitudinal relations with character recognition. The initial question of the dynamic assessment of morphological awareness (DAMA) task asked children to judge whether the first morphemes in a pair of words shared the same meaning. Subsequently, up to four prompts were provided for each word in the word pair to draw children's attention to the meaning of the target morpheme as well as the morphological structure of the word. The participants included 154 first‐grade Chinese children. In addition to the DAMA task, they received a battery of measures including nonverbal intelligence, rapid automatized naming, phonological awareness, vocabulary knowledge, and Chinese character reading. While all measures were administered at the beginning of grade 1, the character reading measure was administered again at the end of grade 1, and at the beginning and end of grade 2. The results showed that the prompts provided in the DAMA task increased children's ability to identify morpheme meanings in compound words by helping them separate morpheme meanings from word meanings. Furthermore, the DAMA task accounted for significant unique variance in character recognition concurrently at the beginning of grade 1 and longitudinally at all three other time points after controlling for static morphological awareness and other reading related variables. Our study suggests that dynamic assessment can be used to effectively assess morphological awareness and predict character reading abilities in young Chinese children.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".