The effects of explicit instruction on French-speaking kindergarteners’ understanding of stories
Bibliographic record
Abstract
The study examines the effects of a short period of explicit instruction on the narrative comprehension of French-speaking kindergarteners, as measured by story retell and comprehension questions. A group of kindergarteners that received explicit instruction ( n = 15) was compared to a control group that was exposed to the same storybooks and afterwards shared related experiences ( n = 15). In the explicit instruction group, comprehension was facilitated through instruction on story grammar, cause–effect relationships, and the internal states of characters. Instructional strategies included explanation, modelling of the identification of story components, guided practice, feedback, and the use of visuals to map story elements, depict causes and effects, and represent internal states. At posttest, children in the explicit instruction group had significantly higher scores on the retell task, as expected, but not on the comprehension questions, a finding we discuss in light of task demands. Although further investigation is needed, the retell results are consistent with findings by others, demonstrating the benefits of instruction on children’s narrative skills. The study is the first we are aware of to assess instruction of brief duration and for French-speaking children, and one of the very few to examine explicit instruction with kindergarteners, regardless of language. Narrative instruction of the kind reported here might be of particular interest to speech-language therapists given the benefits that accrue to children as well as the opportunities that such instruction provides for ‘push in’ service delivery and collaboration with classroom educators.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".