Literacy Environment in Early Childhood Classrooms: Associations with Children’s Engagement
Bibliographic record
Abstract
This study examines associations between the literacy environment of early childhood classrooms and children’s engagement. Children’s language development relies on quality educational practices in preschool and kindergarten. The literacy environment includes a physical dimension (e.g., books, writing materials, environmental prints) and an interactive dimension (teacher-child and child-child interactions). Engagement refers to the quality of children’s individual experiences, including teacher interactions, peer interactions, task orientation, and conflict interactions. Observations were conducted in 30 classrooms using the Early Language and Literacy Classroom Observation Pre-K (ELLCO Pre-K) to assess the literacy environment, along with the Individualized Classroom Assessment Scoring System (inCLASS) to assess 150 children’s engagement. Findings show that the literacy environment was at a basic quality level, while children’s engagement remained in the low range for teacher, peer, and conflict interactions, and in the medium range for task orientation. No significant associations were found between the literacy environment variables and children’s engagement, but socioeconomic status, child age, and group size were associated with children’s engagement. Results are discussed in light of early literacy teaching practices, supporting children’s engagement, as well as how these findings can be incorporated in teacher training.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".