Patterns of teachers’ ongoing learning opportunities in Ontario, Canada: A ten-year qualitative longitudinal study
Bibliographic record
Abstract
This study examines the ongoing learning opportunities for teachers in Ontario, Canada. Using a constructivist approach and a qualitative longitudinal methodology, we analyze two teachers’ self-report on learning for their first ten years of teaching (2004-2014). Our aim was to identify and describe patterns on teachers learning opportunities over time as a way to portrait the complexity and continuity of teachers’ learning during their careers. The findings showed three main learning patterns: (i) learning through courses and workshops, and identifying knowledge gaps, around first to third year of teaching; (ii) identifying, reflecting on, and engaging in their preferred ways of learning, between third and seventh year of teaching; and (iii) evaluating professional development and initiating spaces for learning, between seventh and tenth year of teaching. Further research is needed to gather richer examples of these patterns, but we believe our research contributes to understanding the nature of ongoing teacher learning throughout the career.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".