Teachers’ Ongoing Profesional Learning: A Ten-year Qualitative Longitudinal Study
Bibliographic record
Abstract
This qualitative longitudinal study investigates changes in teachers’ professional learning over the first ten years of their careers. I explore how teachers’ personal lives, their school context, and the larger policy context shaped their professional learning during this period. The main data source consisted of yearly interviews with four Ontario teachers from 2004 to 2014. Five main findings emerged from the analysis. First, the participants’ learning in years 1 to 3 was largely driven by their efforts to survive in the profession, and it was focused on building their classroom teaching practice. Second, I identified a major change in teachers’ learning in their third and fourth years of teaching, specifically the participants developed an ability to become self-directed in their learning. They became more selective in choosing learning opportunities that helped them to diversify their teaching repertoire and to re-examine their vision of teaching. Third, teachers’ learning was shaped by their passions; these focused on subject matter, student engagement, learning, and personal interest. Fourth, the school context and teachers’ learning mutually influenced each other. Whereas the school shaped teachers’ learning, teachers’ leadership roles, while conducting professional development for their colleagues, shaped the context in return. Fifth, the policy context was driven by an accountability agenda that established links between teachers’ learning and students’ achievement on standardized assessments. This mandate guided the policy priorities to focus on the grades and curricular areas assessed (i.e., Grades 3 and 6, literacy and mathematics). This had the effect of restricting teachers’ learning. These findings contribute to our understanding of the ongoing and complex nature of teachers’ learning. One of the major implications for teacher education programs is the acknowledging of the necessity of exploring the connection between the personal and the professional dimensions of teaching through eliciting teachers’ passions. Implications for schools include the importance of promoting conference-like events where teachers can share their pedagogical innovations and learning, and also contribute to whole school learning. Implications for teacher learning policies point to the value of providing space for teachers’ voice and choice in professional development programs.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".