Examining the Professional Learning of Teacher Educators in a Graduate Teacher Education Program
Bibliographic record
Abstract
This paper investigates teacher educators’ professional learning in a graduate teacher education program at a research-intensive university after recent government-mandated changes have affected program size, structure, and staffing needs. Qualitative data gathered from interviews, program documents and field notes are used to examine and report on various mechanisms and processes that contribute to a collaborative faculty culture of learning while building coherence within a large, varied, and changing program. Findings indicate that specific leadership roles built into program structure can facilitate opportunities for professional learning by contributing directly to capacity-building initiatives that foster a culture of de-privatized practice. However, the landscape of precarious employment hinders most collaborative learning efforts. Additionally, teacher educators were interested in a range of learning designs and activities that contributed to their evolving teaching and learning practice. They included observing colleagues teach, co-teaching, co-planning and a willingness to share strengths by leading a learning session. Findings suggest that greater attention must be paid to how leadership manifests itself at different levels in program design and how teacher educators continue to negotiate their on-going learning and growth.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".