A Systematic Review and Meta-Analysis of a Measure of Staff/Child Interaction Quality (the Classroom Assessment Scoring System) in Early Childhood Education and Care Settings and Child Outcomes
Bibliographic record
Abstract
The quality of staff/child interactions as measured by the Classroom Assessment Scoring System (CLASS) in Early Childhood Education and Care (ECEC) programs is thought to be important for children's outcomes. The CLASS is made of three domains that assess Emotional Support, Classroom Organization and Instructional Support. It is a relatively new measure that is being used increasingly for research, quality monitoring/accountability and other applied purposes. Our objective was to evaluate the association between the CLASS and child outcomes. Searches of Medline, PsycINFO, ERIC, websites of large datasets and reference sections of all retrieved articles were conducted up to July 3, 2015. Studies that measured association between the CLASS and child outcomes for preschool-aged children who attended ECEC programs were included after screening by two independent reviewers. Searches and data extraction were conducted by two independent reviewers. Thirty-five studies were systematically reviewed of which 19 provided data for meta-analyses. Most studies had moderate to high risk of bias. Of the 14 meta-analyses we conducted, associations between Classroom Organization and Pencil Tapping and between Instructional Support and SSRS Social Skills were significant with pooled correlations of .06 and .09 respectively. All associations were in the expected direction. In the systematic review, significant correlations were reported mainly from one large dataset. Substantial heterogeneity in use of the CLASS, its dimensions, child outcomes and statistical measures was identified. Greater consistency in study methodology is urgently needed. Given the multitude of factors that impact child development it is encouraging that our analyses revealed some, although small, associations between the CLASS and children's outcomes.
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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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".