Pre-service teachers becoming researchers: the role of professional learning groups in creating a community of inquiry
Bibliographic record
Abstract
Abstract Contemporary schools seek to employ teachers who are curious learners, who can employ practitioner inquiry skills to investigate, inform and grow their own classroom practice, responsive to their circumstances. As a profession, the question we must ask is how do we best prepare and continue to equip teachers with the necessary research skills to investigate and inform their own practice? In this study, we share our pedagogical stance and features of our approach in a new core undergraduate subject for pre-service teachers (PSTs). We discuss professional learning groups (PLGs) for initial teacher education students as the main intervention in the subject, and, more specifically, we elaborate how regular participation in PLGs formed in an on-campus subject can help PSTs to become researchers. We draw on 183 student exit tickets and student feedback surveys to consider broader implications for how to engage teachers in research. This study poses questions about the nature of practitioner research and investigates the role that PLGs play in disrupting the challenges universities face in preparing teachers to engage in and with research.
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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 | 0.047 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".