Experienced teacher educators hunting assumptions to examine their pedagogy: An international collaborative study
Bibliographic record
Abstract
The research presented in this article focuses on an international collaboration conducted by four experienced teacher educators who used assumption identification and examination to advance pedagogical practice. It describes and examines how teacher educators deliberately undertook reflective practices to inform and enhance teaching. Four vignettes are described and analyzed—Practica woes and Modelling practice—and examined using the simple, complicated, and complex teaching framework. The key outcomes include the impact and role of assumption definition, identification, and examination as powerful reflective tools. Researching practice in teacher education is an effective way to advance pedagogical knowledge and practice and a disposition of inquiry is necessary to enhance knowledge at all stages of teacher educator experience. This international collaboration highlights the importance of problematizing teaching, continually inquiring into and interrogating practice and grasping the teachable moments.
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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 | 0.040 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".