Dialogic reading in an early childhood education setting: ECEs’ learning in the context of a community of practice model
Bibliographic record
Abstract
The professional development (PD) of early childhood educators (ECEs) is key to childcare quality. The Professional Learning Community (PLC) model has been well-researched in K-12 settings and is found to have advantages over traditional models of PD; thus, it holds promise for educators in other settings such as early childhood education centers. Indeed, there is an increasing call for the implementation of this model in ECE settings. Some research has found that supporting ECEs’ professional learning (PL) through PLC development is a promising approach; nonetheless, PLCs remain under-researched in early childhood contexts, and findings are less conclusive. The current study examined the potential of PLC development for supporting learning about Dialogic Reading (DR) – a topic of interest to ECEs. In the current study, ECEs in one Canadian context participated in an online project in which they concurrently learned about the PLC model and DR. Our research, including ECEs’ written reflections, and video data of the ECE-child storybook reading, yielded novel findings. These findings suggest that ECEs learned to employ the specific techniques of dialogic reading for engaging children and enriching the dialogic interactions during storybook reading, while also learning to enact processes associated with a PLC model. The ECEs attributed several features of the PLC model to deepening their DR learning. These findings and their implications for practice and future research are further considered.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".