Bibliographic record
Abstract
Teacher professional development is often promoted as a panacea for improving schools but it rarely lives up to its promise. This book develops an approach to professional learning that has motivated teachers and resulted in impressive improvements in student learning, particularly for students who traditionally underachieve in school. As the underpinning research shows, the approach has proven successful in several countries with consistently positive outcomes. The book begins by identifying the shifts that need to happen for the power of professional learning to be realized in promoting student engagement, learning and well-being. Some shifts in thinking include: Moving from professional development to professional learning Putting student learning at the heart of professional learning Focusing on the knowledge and skills to be learned rather than the forms of delivery Focusing on the double-demand of being both immediately practical and developing underlying principles so that new problems can be solved in the future At the heart of the book is an inquiry and knowledge building cycle for teachers that is central to realizing the power of professional learning. The cycle is underpinned by research on how people learn and what it means to be a professional. Later chapters identify what leaders in schools and beyond can do to support teacher learning in ways that directly benefit students. Throughout the book there are case examples of real experiences of primary and secondary teachers and leaders across three countries (UK, Canada and New Zealand) that bring alive what it means to engage in professional learning in ways that excite teachers and benefit their students. This is essential reading for teachers who want to take control of their own professional learning in ways that make a difference to their students and school leaders with responsibility for promoting professional and student learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".