Towards Variation in Professional Learning Practices: A Case Study on Collaborative Inquiry with Language Teacher Candidates
Bibliographic record
Abstract
Creating conditions that allow for the autonomy of educators during professional learning has been a key focus in educational research in recent years. Much evidence supports that collaborative inquiry (CI) is a model that can foster this autonomy by prioritizing teachers’ concerns, needs, and interests in the professional learning process. By working on problems they encounter in their daily experiences, groups of educators are able to develop unique professional learning practices that attend to their context. Through the lens of complexity, this study examines how variation emerged in the CI practices of four groups of teacher candidates who shared the common discipline of language teaching. The data were collected with participants through interviews, video recordings, and a researcher journal, then reconstructed into a narrative case study to showcase the unique learning trajectories of each of the CI groups. I discuss the nuances between these trajectories and the implications for CI initiatives with language educators.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".