What do 'good' teachers know? Investigating teacher professional knowledge
Bibliographic record
Abstract
‘Everyone remembers a good teacher’ was the theme of a recent teacher recruitment campaign in the United Kingdom, but what is it that is considered ‘good teaching’in science and technology? Drawing on empirical work carried out with teachers in Australia, Bangladesh, Canada, Finland, India, Iraq, New Zealand and the United Kingdom this paper sets out aspects of teacher professional knowledge by presenting a framework in the context of design and technology education and generalizing it to a common frame of analysis. What constitutes the school science and school technology curriculum has gone through considerable change in many countries over the last twenty years and the analytical framework presented here can be shared with teachers to enable them to use it as a tool to focus on their own professional development needs and personal beliefs about successful teaching. Considering their subject knowledge, pedagogical knowledge, ‘school knowledge’ and their own rationale for the teaching of science or technology, teachers are able to articulate their professional priorities. Satisfying those professional needs, however, in an environment where teachers are ‘time-poor’ and under considerable pressure to be in school working day by day with their students to achieve examination results is an acute challenge for teacher educators and policy makers. The paper considers some on-line open and distance learning as a model for effective school-based teacher professional development. By making the school itself the site of learning and the classroom, laboratory or workroom the arena of change,teacher professional growth can not only become effective but cost-effective.
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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 | 0.020 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".