Intervenir dans le jeu symbolique des enfants d'âge préscolaire pour soutenir leur développement langagier
Bibliographic record
Abstract
Pretend play plays an invaluable role in the development of kindergarteners and enables their language development. However, play time is decreasing in kindergarten due to an increased emphasis on adult-directed academic activities. This could be explained by the need for kindergarten and preschool teachers to develop their ability to intervene in pretend play to scaffold children's oral and written language. To meet this need, an educational design research was carried out with the aim of developing a pedagogical device based on the Vygotskian perspective. First, this article describes how, with anasynthesis, we created a flowchart presenting eight interventions to be conducted in the pretend play to scaffold children’s language development. Second, it presents the usefulness of this tool according to three kindergarten teachers who try it out in their class for 10 to 12 weeks. Data were collected with the think-aloud method, two semi-structured interviews and one focus group. A qualitative analysis allowed to identify the interventions that have proven useful in scaffolding the children’s language development, to describe to what extent the flowchart has made it possible to meet the needs of teachers, to highlight the interest of children in using the writing and in doing writing attempts, as well as children's progress in language development. Thus, the results presented in this article contribute to the operationalization of Vygotsky's ideas in the educational environment concerning the development of language and pretend play.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".