Principles for effective pedagogy : international responses to evidence from the UK Teaching & Learning Research Programme
Bibliographic record
Abstract
Introduction Mary James and Andrew Pollard Chapter 1. TLRP's ten principles for effective pedagogy: rationale, development, evidence, argument and impact Mary James and Andrew Pollard Chapter 2. Pedagogy, didactics and the co-regulation of learning: a perspective from the French-language world of educational research Linda Allal Chapter 3. Commonalities and differences: some 'German' observations on TLRP's ten principles for effective pedagogy Ingrid Gogolin Chapter 4. Contributions to innovative learning and teaching? Effective research-based pedagogy - a response to TLRP's principles from a European perspective Filip Dochy, Inneke Berghmans, Eva Kyndt and Marlies Baeten Chapter 5. A response from Japan to TLRP's ten principles for effective pedagogy Tadahiko Abiko Chapter 6. Yes Brian, at long last, there is pedagogy in England - and in Singapore too. A response to TLRP's Ten Principles for Effective Pedagogy David Hogan Chapter 7. A response from Canada to TLRP's ten principles for effective pedagogy Lorna Earl
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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 | 0.064 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".