Building, Supporting & Assuring Quality Professional Practice: A Research Study of Teacher Growth, Supervision, & Evaluation in Alberta
Bibliographic record
Abstract
Alberta is considered among the world’s top performing education systems. Over the past two decades, the provincial education system has invested heavily in building teachers’ professional capital to ensure that the quality of teaching in Alberta is among the best in the world. A wealth of the educational reform research literature, at both international and provincial levels, suggests that continuous professional learning is key to building teachers’ professional capital. Within Alberta, the Teacher Growth, Supervision, and Evaluation Policy (TGSE) (Government of Alberta, 1998) guides that learning. In 2017, Alberta Education requested a comprehensive research study to inform an update to the existing policy, and to identify associated requirements for the growth, supervision, and evaluation of principals and superintendents. This research study provides an independent, objective examination of TGSE in Alberta school authorities and related policies at the school authority level. The purposes of the study were to provide education stakeholders and the Ministry with • an independent, objective review of the provincial TGSE Policy in Alberta and of related policies at the school authority level; • recommendations on how best to support implementation of any proposed changes to the TGSE policy; • recommendations on how the TGSE model should inform related policy on growth, supervision, and evaluation of principals; and • recommendations on how the TGSE model should inform related policy on growth, supervision, and evaluation of superintendents and school authority leaders. Research Design: The eight-member research team from the universities of Calgary, Lethbridge, and Alberta adopted a concurrent mixed methods research design to generate insights into educator experiences with and perspectives on teacher growth, supervision, and evaluation within the TGSE policy context. Our comprehensive analysis and merging of the study’s quantitative and qualitative data generated 14 merged findings and 10 recommendations. Quantitative data were generated from online surveys of 710 teachers, 131 principals, and 33 superintendents. Analysis of the survey data provided province-wide insights from a large population of educators in June and July of 2017. Qualitative data were gathered through multiple case study research during March to June of 2017. Members of the research team conducted individual and/or focus group interviews of teachers (n=64), principals (n=53), superintendents, and other system leaders (n=33) in seven randomly-selected school jurisdictions and selected charter and independent schools. Nine individual cases illustrated and illuminated practices through which teachers and leaders at the school and administrative levels engaged in teacher growth, supervision, and evaluation in their unique contexts. Our cross-case analysis identified 13 larger themes. Evidence was gathered in two additional ways: (a) through analysis of 30 randomly-selected school authority policies, and (b) through interviews of education partner organization leaders. The team also gathered evidence from documentary sources, artifacts, and field notes.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".