Enhancing Instruction in Inquiry-Based Early Literacy Classrooms
Bibliographic record
Abstract
Ontario’s Kindergarten Program document (Ontario Ministry of Education, 2016) advocates for student-directed and inquiry- and play-based pedagogies to support four- and five-year-old children’s learning. In practice, educators’ understanding and implementation of inquiry-based pedagogies varies considerably. Our study sought to bridge theory and practice through collaboration between a faculty of education and a local school board to support pre- and in-service educators’ understanding of inquiry-based pedagogy. It also sought to help these teachers integrate opportunities for embedded literacy instruction. We used classroom observations, pre- and post-surveys and workshops to determine educator and teacher candidates’ understanding of inquiry and early literacy. Overall, educators expressed a positive inclination towards inquiry-based pedagogy and early literacy instruction; however, their implementation of these varied. Through concrete learning experiences, reflection and facilitation, educators’ understanding improved and they began to implement ideas from the workshops into their practice. Our results highlight the need to improve training and support for kindergarten educators to enable them to implement inquiry-based pedagogies effectively and build vital literacy skills through embedded learning. This has direct implications for local and provincial policy and for children’s ability to learn, build skills and become successful readers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".