Associations of Specific Indicators of Adult–Child Interaction Quality and Child Language Outcomes: What Teaching Practices Influence Language?
Bibliographic record
Abstract
Research Findings: This study aims to extend our knowledge regarding contributions of educator-child interactions to child language outcomes by examining the extent to which specific dimensions of the CLASS observational tool of educator-child interactions are associated with child language abilities, utilizing data from an Australian longitudinal study of over 2,000 children attending formal Early Childhood Education and Care (ECEC).The analysis included a novel measurement model fitted to the data to allow each CLASS dimension to be modeled separately.Results showed that each CLASS dimension was associated with initial average language abilities.Small, negative effects of Emotional Support dimensions on growth of children's average Understanding Directions score were found, but there were no associations between any of the dimensions and average growth in Verbal Ability.None of the Instructional Support dimensions (which are language focused) predicted growth in language abilities.These null findings are addressed in the discussion.Practice or Policy: Findings from this study illustrate that, typically, ECEC programs rate low on dimensions of quality developed to capture language-promoting educator-child interactions.Findings also suggest a selection effect related to equity of access to classroom quality with children with the highest initial language abilities in the highest quality classrooms.There is a growing body of evidence to suggest that high-quality early childhood education and care (ECEC) can have a significant impact on children's language development and predicts better achievement throughout childhood and into adulthood (Cabell et al., 2015;Ulferts et al., 2019;Yoshikawa et al., 2013).Quality in the ECEC context is multidimensional and often characterized by the dimensions of structural and process quality.Structural quality encompasses the regulatable aspects of a program such as staff/child ratios and staff qualifications that are expected to support high-quality ECEC (Howes et al., 2008).Process quality refers to the quality of children's day-to-day experiences and interactions with educators, other children and materials, and their participation in learning experiences that are associated with children's learning and development (Howes et al., 2008;R. Pianta et al., 2005).Both theory and program evaluations, including the Abecedarian and Perry preschool programs, provide evidence
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".