Examining the Relationship between Preschool Teachers’ Perceptions about Early Literacy and their Implementation Practices
Bibliographic record
Abstract
The purpose of this qualitative study was to examine the relationship between preschool teachers’ perceptions about language and early literacy practices and independent observations of their implementation of those practices in a classroom setting. Semi-structured interviews and classroom observations were conducted with five preschool teachers who participated in an Early Reading First (ERF) program. The teachers were purposively selected based on differences in their level of fidelity implementation of instructional practices. Three teachers were selected from high fidelity (HF) of implementation group and two teachers were chosen from low fidelity (LF) group. The three research questions were related to 1) teachers’ beliefs about the effect of ERF program on children’s early literacy outcomes, 2) changes in teachers’ instructional behaviors resulting from ERF, and 3) teachers’ beliefs about individualizing early literacy experiences according to children’s ages or developmental level. Based on the interview and observations, three themes were found: (a) importance of preschooler’s early literacy experiences, (b) changes in teaching behaviors, and (c) changes in early reading and writing instruction according to children’s ages or developmental level. Implications and limitations were discussed in terms of using the information of teachers’ perceptions into professional development training to increase teachers’ early literacy practices.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| 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".