Professional learning communities: the journey from <i>‘do we HAVE to go there’</i> to <i>‘teachers getting together and being colleagues</i>
Bibliographic record
Abstract
This paper explores persistent obstacles undermining in situ teacher professional development as well as possibilities for learning and growth in a professional learning community (PLC). We extend the existing scholarship in this field by examining how teachers' discursive practices can predict the success or failure of a PLC initiative. We used finegrained conversational analysis, focusing on teachers' talk to generate insights into how teachers position themselves and define their participation in a PLC, telegraphing possible outcomes of a PLC initiative. We followed the professional trajectories of four in-service high school science teachers over two years. We examined how their collaborative work shaped teachers' identities, practices, and agentive actions to elevate their classroom practice. Attending to teachers' discourses about their professional learning needs when inviting them to engage with their professional development is crucial for meaningful engagement in this process. Moreover, developing opportunities for them to take ownership of the PLC's structural underpinnings and lead their learning process help to generate productive learning spaces promoting professional growth. This research informs us how teachers can support each other in developing their identity and agency regarding their learning and engagement in pedagogic work.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".