Pre-Service Science Teachers’ Scientific Reasoning Competencies: Analysing the Impact of Contributing Factors
Bibliographic record
Abstract
Abstract Scientific reasoning competencies (SRC) are one part of science teachers’ professional competencies. This study examines the contribution of three factors to the development of pre-service science teachers’ SRC: the amount of science education classes, the amount of science classes and the pre-service science teachers’ age. The factors amount of science education classes and amount of science classes have been operationalised in terms of ECTS credit points. N = 438 pre-service science teachers from six universities in Germany, Chile and Canada voluntarily and anonymously responded to an established multiple-choice instrument for assessing SRC, which has been developed by the authors and is available in German, Spanish and English. Multiple linear regression analyses show that the included factors explain a proportion of about 9% of the pre-service science teachers’ SRC. The factor amount of science classes is the only significant predictor and can be seen as an indicator of learning science content knowledge. These findings support the assumption of science content knowledge being a prerequisite for developing pre-service science teachers’ SRC.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".