Profile of teacher-child interaction quality in groups of three-year-old children in Quebec and France
Notice bibliographique
Résumé
Early childhood is widely regarded as a critical period for children’s development and academic success (April et al. 2018; Bouchard et al. 2017). In fact, the child’s brain development is highly affected by new experiences (Simard et al. 2013; Yoshikawa et al. 2013), making high-quality educational environments paramount in fostering children’s success in life. Even though educational quality encompasses several variables, a meta-analysis by Sabol et al. (2013) found that adult-child interaction was the best predictor of children’s later outcomes. However, research also shows that educational childcare services (ECS) around the world rarely offer high interaction quality environments (Slot, 2018; Tayler et al., 2016). Hence, several nations have set up quality assessment practices (OCDE, 2015). In the French speaking community, Quebec and France have developed such practices, but show different cultural, political and social contexts that can lead to discrepancies in how interaction quality is actually applied in their ECS. To explore this possibility, a study by Author et al. (2019) was conducted and found that interaction quality in Quebec’s ECS was significantly higher than in France’s ECS. However, their analysis was based on a variable-centered approach using means, which may create an inadequate representation of reality (Haccoun and Cousineau 2010). Using a secondary analysis of data (Author et al. 2019), this study thus aimed at identifying latent profiles of adult-child interaction quality in groups of three-year-old children in Quebec early childhood centers and French kindergarten classrooms, as measured by the CLASS Pre-K. This study also aimed to explore existing associations between identified interaction quality profiles and structural characteristics (staff qualifications, ages, group size). Latent profile analyses showed three interaction quality profiles in Quebec, with most of the participants (52,5 %) in the highest-quality profile, and four interaction quality profiles in France, with participants almost evenly distributed between profiles. These results suggest more homogenous teacher training in Quebec than in France. The scores of the three CLASS Pre-K domains associated with identified profiles show a higher average interaction quality in Quebec compared with France. As for characteristics of structural quality, our analyses suggest that only the group size variable is significantly associated with scores of interaction quality, and exclusively so with the “medium-quality” (MQ-FR) and “medium-low quality with emphasis on classroom organization” (MLQ-CO) profiles in France. Thus, group size of French kindergarten classrooms associated with the MQ-FR profile is significantly lower than the group size of French classrooms associated with the MLQ-CO profile. This suggests that, in French kindergartens context, group size reduction could allow groups associated with the MLQ-CO profile to find themselves in the MQ-FR profile. The highest French interaction quality profile could then account for more than 50% of the sample, which would be a significant improvement in the average quality of kindergartens. Other studies are nonetheless required in order to confirm this hypothesis.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».