Stages of teaching expertise from routine to adaptive: A model for advancing teaching effectiveness
Bibliographic record
Abstract
This cross-sectional study evaluated the implementation of a professional practice standard for teaching over four years in a top-performing Canadian education system with 62 school districts and approximately 35,000 teachers in the system. Using a convergent mixed methods research design, quantitative data were generated from online surveys with 5536 teachers and qualitative data were gathered through focus group interviews with teachers ( n = 193). Results from the study have been used to inform an update to the Teaching Effectiveness Framework (Friesen, 2009) and provide insights into a model for advancing teaching effectiveness. Conceptual stages were developed to illustrate teacher progression from routine to adaptive expertise, measured on a four-point scale. The scale is introduced as a resource to support the integration of policy initiatives into actionable practices and professional learning. Results underscore the value of integrating research literature with teacher and student perspectives to conceptualize and advance teaching effectiveness principles. • Results provide a model for advancing teaching effectiveness. • Framework charts conceptual stages from routine to adaptive expertise. • Study provides a system-oriented perspective for the conceptualization of teaching effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".