5 Chapter Five -- Optimum Learning Literature Synthesis: Supporting the Implementation of Standards
Bibliographic record
Abstract
This synthesis of the literature is designed to undergird our 4-university longitudinal mixed methods study Optimum Learning for All Students Implementing Alberta’s 2018 Professional Practice Standards. Our ambition is to gain insights into how and how well Alberta’s Teaching Quality Standard, Leadership Quality Standard, and Superintendent Leadership Quality Standard are being put into place, how the standards are impacting practice, and what changes occur over time in teaching and learning. Indeed, our longitudinal design is premised on “uncovering sustained changes and implementation success” (Derrington, 2019, p. 8). Given this, our goals in preparing this manuscript were to (a) synthesize scholarship on policy processes so that we can situate our inquiry into the standards in a process-oriented way; (b) provide a jurisdictional review of standards-based approaches to teaching and leadership and what we know to be effective with respect to this approach so that we can discern how Alberta’s standards and pathways to certification are positioned compared to others who have gone before us; and (c) synthesize scholarship that demonstrates the link between the professional practice standards and quality teaching and leadership so that we are anchored to evidence when interpreting the forthcoming empirical data. Considering the comprehensiveness of the professional practice standards, we covered the waterfront, so to speak. But though we plumbed many strands and sources of knowledge, we do not claim it to be exhaustive or necessarily complete.
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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 | 0.097 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".