An Alternative Model of Continuing Professional Development for Teachers: Giving Teachers Time
Bibliographic record
Abstract
The paper reports on the outcomes of a Department of Culture, Museums and Sport (DCMS) funded project which provided resources for three groups of teachers in different subjects and age phases to have some time where they were freed from their teaching responsibilities, and also given time to meet together with other teachers to share ideas. The idea underpinning the project was to explore a model of continuing professional development for teachers which was different in approach to recent Department for Education and Skills (DfES) ‘strategy’ and training based approaches.The three groups of teachers (primary science, secondary history and secondary science) met together with curriculum tutors from the local School of Education to explore ideas about how to develop innovative approaches in aspects of their subject teaching. In addition to funding four days of supply cover for the teachers involved to meet, the teachers were also given at least two days of supply cover during the course of the project to develop their ideas. Towards the end of both academic years, the groups met again to share their ideas. The paper describes the outcomes of the project and the teachers’ perceptions of the process issues and problems involved in pedagogical innovation. The paper also considers some of the broader issues arising from the project, in terms of how to make the most effective use of teachers’ time, in an era when there are many competing demands on this finite resource.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| 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".