Modern Requirements to the Content Selection of Teaching Physics and Mathematics, Aimed at the Development of Design and Technical Competence of Technical University Students
Bibliographic record
Abstract
The research’s relevance of the defined in the article problem is due to the fact that when the study of physical-mathematical disciplines the consolidation of the acquired knowledge occurs and the development of skills contribute to their using in manufacture problems’ solving in the professional activities of the engineer. In this regard, this article is aimed at the development of modern requirements to the content selection of teaching physics and mathematics, aimed at the development of design and technical competence of technical University students. In the study of this problem the modular competency approach is set out, which allows on the base of the required competencies of future technical specialists to identify the following basic requirements for the selection of the content of teaching physics and mathematics: the disciplines’ integrity and fundamentality, systematic and consistent presentation of educational material, problematic and innovativeness of their content, their interdisciplinary, professional orientation, orientation on the formation of logical thinking of students. The article can be useful in selecting and structuring the content of teaching physics and mathematics courses in high school, as well as in the future teachers’ training of these disciplines.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".