Cultural Differences and Latent Profiles from the Individualized Classroom Assessment Scoring System Observational Tool (inCLASS).
Bibliographic record
Abstract
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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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".