A Study of Teacher Growth, Supervision, and Evaluation in Alberta: Policy and Perception
Bibliographic record
Abstract
Teacher effectiveness has long been identified as critical to student success and, more recently, supporting students attaining the skills and dispositions required to be successful in the early 21st century. To do so requires that teachers engage in professional learning characterized as a shift away from conventional models of evaluation and judgment. Accordingly, school and system leaders must create “policies and environments designed to actively support teacher professional growth” (Bakkenes, Vermunt, & Webbels, 2010). This paper reports on the Alberta Teacher Growth, Supervision, and Evaluation (TGSE) Policy (Government of Alberta, 1998) through the eyes of teachers, school leaders, and superintendents. The study sought to answer the following two questions: (1) To what extent, and in what ways, do teachers, principals, and superintendents perceive that ongoing supervision by the principal provides teachers with the guidance and support they need to be successful? and, (2) To what degree, and in what ways, does the TGSE policy provide a foundation to inform future effective policy and implementation of teacher growth, supervision, and evaluation? Results affirm international findings that although a majority of principals consider themselves as instructional leaders, only about one third actually act accordingly (OECD, 2016).
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".