Models and Mathematical Modelling: What Do Teachers and Preservice Teachers Know?
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
The aim of the research is to examine the perspectives of teachers and preservice teachers in regard with models,mathematical models and mathematical modelling process in different variables terms and to compare them. In thisresearch that is having quantitative research design survey method, which is one of the descriptive research technic,survey method is used. Research is performed with 127 teacher and preservice teachers. It is benefitted from adifferent survey in the stage of data collection and open ended questions that is developed by the researcher. In theanalysis of the data, descriptive and inferential statistical methods and content analysis method were used. As a result,views of the teachers and preservice teachers regarding (mathematical) models and mathematical modelling showsdifferences according to some variables, however, it is not found out most significant differences in the views of theteachers and preservice teachers in regard with these subjects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it