A Decade of Evolution in the Practice Teaching Component Of a Canadian Teacher Education Program: What Drove Change, What Insights Were Gleaned, And What Challenges Lie Ahead
Bibliographic record
Abstract
This paper explores the many and diverse factors that have driven changes to the field-based component of a program of teacher education in a large Canadian university over the past 10 years. We look at key contexts of influence to any work in program change; that of internal and external bodies at the institutional level, the operational or program delivery level, feedback from stakeholders in teacher education, and lastly, the research and scholarship in teacher education that provides a framework for innovation and improvement. A significant inclusion in this paper is the perspective on the support at ground level that is required, but rarely acknowledged, to move any change initiatives forward. We present models that capture a decade of change to 4 undergraduate field experiences and 2 graduate level internships that comprise the field component of our teacher education program. We share key indicators of success resulting from these changes, highlight important insights gleaned, and point to pressing challenges that we face as we look to future program evolution that will carry us into the next decade. While this work reflects one single field-based component of a teacher education program in Canada, we trust that useful parallels can be drawn by the reader as they contemplate or are in the process of addressing and moving through similar orbits of change and evolution within their own 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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".