Cultural Differences and Latent Profiles from the Individualized Classroom Assessment Scoring System Observational Tool (inCLASS).
Notice bibliographique
Résumé
As preschool turns into a typical setting in early childhood education (ECE), it seems essential to better understand the nature of children’s preschool experiences. Consequently, it is essential to consider an individualized analysis of each child’s experiences about their task engagement within the classroom. Studies have suggested that the Individualized Classroom Assessment Scoring System (inCLASS) has the potential to provide an “authentic, contextualized assessment of young children’s classroom behaviors” (Downer, Booren, Lima, Luckner, & Pianta, 2010). However, more knowledge is needed regarding the cultural differences of the inCLASS, particularly internationally. \nThe present study aims to compare how the dimensions of the inCLASS differ in two different countries: France and Canada. It also aims to determine the latent profiles of the four domains of the inCLASS. The sample included 404 children observed with the inCLASS divided in two groups, 201 children (103 girls) from CPEs group (M= 38.06 months, SD = 2.32) and 203 children (100 girls) from kindergarten group (M = 40.52 months, SD = 2.43). \n\tUsing t-tests and Mann-Whitney tests, we found significant differences between groups on the following dimensions: Positive engagement with adult where Canada’s childcare CPEs were found to have obtained higher scores compared to France kindergartens, t(402) = 3.67, p = < .001, r = 0.18, Communication with adult where Canada’s childcare CPEs were found to have obtained higher scores compared to France kindergartens, t(378.90) = 5.79, p = < .001, r = 0.29, Behavioral control on task where Canada’s childcare CPEs were found to have obtained lower scores compared to France kindergartens, t(402) = -7.74, p = <.001, r = 0.36 and Interaction domain with adult where Canada’s childcare CPEs also were found to have obtained lower scores compared to France kindergartens, t(390.20) = 5.06, p = <.001, r = 0.25 (See Table 1). \nA Latent Profile Analysis (LPA) was used to create profiles of children’s engagement with teachers, peers, and tasks. One, two-, three-, four- and five-profile solutions were examined. Results indicated a three-profile solution (BIC = 3832, ABIC = 3832.5, VLMR and Adjusted LRT p value = .0601; entropy = .71). Consistent with Willingford et al. (2013), we highlight that 46,4% (n = 187.46) of the sample was classified into the Typically-Engaged profile, characterized by relatively low positive engagement with teachers and peers, moderate engagement with tasks, and low negative engagement. The second profile, labeled Positively-Engaged profile, included 41,5% of the sample (n = 167.66), consisting of children demonstrating significantly higher positive engagement with teachers, peers, and tasks and little negative engagement compared to the Typically-Engaged profile. Finally, the Middle-Negatively-Engaged profile, included 12,1% of the sample (n = 48.88) and displayed lower task and higher negative engagement, but higher teacher and peer engagement compared to the Positively-Typically-Engaged profile (see Figure 1). \nThe present study suggested some cultural variation on an international level regarding inCLASS dimensions. It also indicated that children, while being examined on an individual level, can also be clustered into some different profiles than those already documented (Willingford et al., 2013).
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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 | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».