Pedagogical context knowledge: Toward a fuller understanding of what good science teachers know
Bibliographic record
Abstract
Abstract A codified model of teacher knowledge, situated in school science teaching, is proposed as a synthesis of a number of models, metaphors, and notions already described in the literature about teachers' knowledge. This model, called pedagogical context knowledge, suggests that in discussion of their classroom practice, exemplary science teachers utilize four kinds of knowledge: academic and research knowledge, pedagogical content knowledge, professional knowledge, and classroom knowledge. The model is used to examine data collected through interviews with science teachers about the ways in which they design and implement science lessons. Analysis of the data shows that the model is sufficiently robust to provide a simple and rapid, yet effective and efficient way of examining teachers' views and the knowledge base in which they are embedded. © 2001 John Wiley & Sons, Inc. Sci Ed 85:426–453, 2001.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".