Dialogic Reading with Mandarin-Speaking Children: Effects on Expressive Vocabulary and Narrative Competence
Bibliographic record
Abstract
This research study examines the effects of a home-based dialogic reading (DR) intervention on the expressive vocabulary and narrative competence of Mandarin-speaking children. DR is a shared-reading approach that emphasizes verbal interactions between adults and young children. Expressive vocabulary is generally conceived as children’s knowledge of word meanings in speaking or writing, and oral expressive vocabulary is focused on in the present study. Narrative competence refers to their ability to produce and comprehend stories. Three aspects of narrative production were measured—macrostructure, microstructure, and evaluation. Narrative comprehension was measured using comprehension questions that targeted story grammar and problem resolution. In the present study, 81 Mandarin-speaking kindergartners, aged four to five, were pretested on expressive vocabulary and narrative competence and assigned to one of two conditions—DR or customary reading. Parents of the children assigned to the DR group participated in three DR workshops and conducted at least 48 one-on-one shared-reading sessions at home with their child during the 12-week intervention period. Expressive vocabulary and narrative competence were retested immediately after the intervention and again four months later. In-home observations of shared-reading sessions recorded on video were used to track the changes in parents’ reading behaviours, and multiple measures were employed to collect program-evaluation data. The DR intervention yielded a greater effect on the children’s expressive vocabulary at the delayed posttest stage. However, its effect on indicators of narrative competence was not significant except for one indicator of narrative microstructure—adverb density. Positive changes were found in the reading behaviours of parents in the DR group over the intervention period, and positive feedback was also obtained from the comprehensive evaluation of the DR program.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".