Linking teaching and research in initial teacher education: knowledge mobilisation and research-informed practice
Bibliographic record
Abstract
The link between theory, practice and research in Initial Teacher Education (ITE) has been widely discussed in international literature. However, more needs to be done in regard to the examination of concrete examples to foster research and teaching practice in existing teacher education programmes. This paper focuses on a practicum model in ITE which aims at linking teaching and research, theory and practice. The reflective component of the model is oriented towards student teacher professional development under a democratic view of education. Integrating teaching and research and promoting teaching practice as a space of transformation rather than a process of adaptation or of application of theory may well represent a move towards knowledge mobilisation and research-informed practice. The paper concludes with some lessons learned and possible directions in order to overcome the shortcomings of the model and to enhance its positive and innovative features.Abbreviation ITE: Initial teacher education
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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 | 0.074 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".