Exploring the use of Communities of Practice as Professional Development for French as a Second Language Teachers
Bibliographic record
Abstract
In Canada, French as a second language (FSL) teachers have indicated a lack of professional learning opportunities adapted to their needs and interests. In order to support their ongoing development, more research is needed to study professional learning models that address their unique set of knowledge and skills, such as language proficiency, intercultural awareness, pedagogy, and collaborative professionalism (Masson et al., 2024). To respond to this need, this study implemented a four-month professional development series for FSL teachers in an Ontario school board based on a community of practice (CoP) framework. Data was collected through pre-/post-questionnaires and participant interviews and analyzed through Wenger et al.’s (2011) cycles of value creation. The results show that while the CoP initiative created immediate and potential value for participants, it did not necessarily lead to an applied value, or reported changes to the FSL teachers’ practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".