Building teachers’ capacities one teacher at a time within a learning community framework: A retrospective analysis
Bibliographic record
Abstract
The purpose of this paper is to present how teachers build capacity within a learning community. Two participant researchers, acting as facilitators and co-teachers in an Ontario elementary public school literacy initiative, applied a learning community model for professional development to determine its impact on teachers’ capacity, and on students’ standardized test scores. Data collection included meeting notes from weekly modelling sessions and bi-weekly learning community meetings, field logs, reflection statements from teachers and principal, and documents (such as team-constructed lesson plans and lesson materials). Findings indicated that the use of a learning community to promote collaborative planning, sharing of effective or best practices for teaching, and modelling of literacy components, was valued by teachers. As well, the collaborative learning experience encouraged teachers to take on increasing responsibilities for planning and delivering lessons, promoting a cohesive learning situation for students, as indicated by significantly improved standardized test scores as measured by the Education Quality and Accountability Office Test (EQAO Test), and staff attitudes towards the use of the learning community, as a means of professional development.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".