How engagement with research changes the professional practice of teacher‐educators: a case study from the Welsh Education Research Network
Bibliographic record
Abstract
Learning to teach well is a complex task. Teacher‐educators have a significant role to play in enabling students to reflect critically and analyse their own practice. However, there is a danger, as a result of funding pressures and other contrary factors, that many institutions that provide teacher education will become separated from their research base. The Welsh Education Research Network (WERN) is a pilot project funded by the Higher Education Funding Council in Wales and the Economic and Social Research Council with the aim of developing educational research capacity in Wales. This paper provides an analytical account of one research group of teacher‐educators funded by WERN. The case study describes the research activity of the group and the views of its members on its impact for their professional practice. Finally an analysis of the findings concludes that engagement with research has resulted in positive changes to the knowledge, skills and critical awareness of the teacher‐educators which has in turn brought benefits to the learning of their students.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".