Teacher's Professional Competence: New Challenges, Realities And Prospects
Bibliographic record
Abstract
Changes in the global labour market apply new requirements to participants in labour relations. Currently, experimental work on the formation of competencies of the 21st century is actively carried out in different countries, including Australia, Canada, Russia, the USA, Finland, South Korea and others. Researchers note that a significant part of professional tasks demands from specialists to have soft skills presented in the "4C" model. These challenges define new requirements for teachers at different levels of education. The Russian system of teacher education reflects global trends in determining priority development vectors: today it is necessary to “equip” a teacher with such competencies, technologies, and skills that will provide learning outcomes that are adequate to the requirements of the 21st century. The purpose of this article is to select and justify modern learning outcomes and the corresponding pedagogical competencies as tools for solving the problems facing modern Russian educational practice based on the analysis of foreign and domestic studies, the analysis of the request for educational practice. In accordance with the purpose, the article presents an analysis of foreign and domestic studies on the problem of highlighting and structuring "new learning outcomes" in the training of teachers; an expert analysis of the results of designing educational models and a comparative analysis of organizational decisions in the system of Russian and foreign education.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".