Professional learning of instructors in vocational and professional education
Bibliographic record
Abstract
This article presents insights from a study into instructor professional learning in vocational and professional education (VPE) in Canada. While most studies on instructor learning focus on learning through formal professional development programmes, this study specifically focuses on professional learning as it happens in day-to-day practice. Analysis of 116 learning episodes reported by 27 instructors from various institutes for VPE shows that instructor learning is mainly focused on developing pedagogical content knowledge (PCK). Learning episodes studied were often externally prompted, not self-directed and involved mostly action-oriented reflection. Ellström’s theory of adaptive and developmental learning is used to further explain these findings. Because of the specialized nature of the content taught in VPE programmes, formal training in PCK is often not available; instructors rely on trial and error, student feedback and peer feedback to develop PCK. Educational leaders within institutes for VPE should consider encouraging professional development models that include collegial dialogue, such as mentoring and communities of practice, as well as the implementation and enactment of professional learning plans. Further research could focus on how existing workplace practices may be enhanced to further support instructor professional learning.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".