Implementing High-Leverage Influences from the Visible Learning Synthesis: Six Supporting Conditions
Bibliographic record
Abstract
Even though there is a plethora of research that can be used by educators to inform their practice, the deep implementation of evidence-based strategies remains unrealized in many schools and classrooms. The question we set out to answer was: What conditions help encourage educators to implement and adapt evidence from the Visible Learning synthesis when they encounter it? We examined two examples of the reception of the Visible Learning research in schools and identified the following six key conditions that helped foster the translation of the Visible Learning research into classroom practice in ways that demonstrated measurable impact on student learning: (1) The presence of a learning methodology; (2) clear examples of how to apply the strategies; (3) a ‘knowledgeable other’ to help assist educators in processing the research; (4) a supportive organizational environment; (5) the recognition of educators as agents of influence, and (6) the monitoring and adjustment of implementation strategies.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; both teacher heads agree on what is shown here.
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".