Cultivating reflective teachers: Challenging power and promoting pedagogy of self-assessment in Australian, Bhutanese, and Canadian teacher education programs
Bibliographic record
Abstract
In this article, we look at three teacher education programs across three countries—Australia, Bhutan, and Canada—to examine how reflection is cultivated in pre-service teachers (also referred to as teacher candidates) through a pedagogy of self-assessment. We begin from the premise that a cornerstone of effective teaching is the capacity of an educator to reflect on their practice and to use their reflections for professional growth and development. Qualitative data were collected from teacher candidates from one teacher education program in each country to obtain the views and reflections of teacher candidates about the power and pedagogy of self-assessment to inform their learning and development. Analysis of results led to three overarching themes: (a) consistent learning priorities of pre-service teachers as they engage with reflection; (b) pedagogical features that leverage self-assessment strategies to enhance reflective practice; and (c) the possibilities for reflection to facilitate a professional stance towards learning. Each theme is discussed with consideration for teacher education practices and theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".