Forms of reasoning used by prospective physical sciences teachers when explaining and predicting natural phenomena: The case of air pressure
Bibliographic record
Abstract
The co‐ordination between theory and evidence is an outstanding characteristic of scientific thinking. Research indicates that students often have difficulty in explaining natural phenomena because they use their own theories to explain phenomena or they are unable to build a bridge between theory and evidence. Science teachers must teach students to collect and select evidence and to use theory to explain it. The objective of this study was to investigate the forms of reasoning used by prospective physical sciences teachers when they build up explanations and make predictions about natural phenomena. Thirty‐eight prospective teachers answered a questionnaire structured around three problems focusing on phenomena that can be explained through air‐pressure variation. The results seem to indicate a variation in the forms of reasoning used, depending on the problem and the type of request. The number of prospective teachers who consistently use a certain form of reasoning is higher within problems than across problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".