Scientific Reasoning Competencies: a Case of Preservice Teacher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this study, we analysed the scientific reasoning competencies of preservice science teachers from a Canadian sample at the beginning and end of a science teacher education methods course. The course contained standard topics, such as the nature of science, assessment, and unit and lesson planning in science. The preservice science teachers were asked to reason about two types of problems in a validated pre- and post-questionnaire: investigatory-process problems and problems regarding modeling. Statistical analysis of the data revealed that the course significantly contributed to the development of preservice science teachers’ competencies for those who had two previous degrees compared with those that did not. Furthermore, a greater proportion of teachers were deemed highly competent at planning investigations and testing models than the more generative dimensions of scientific reasoning, such as formulating questions and generating hypotheses. Implications for science teacher education internationally and the movement towards competency-based curricula are put forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it