Peculiarities of foreign experience of training of future teachers in the context of their professional formation
Bibliographic record
Abstract
The process of professional development of a future teacher cannot be successfully carried out without its combination with advanced foreign technologies of professional training and their reflection in domestic state and regional programs of the development of pedagogical education. Progressive achievements of the foreign countries (Germany, France, Canada, Denmark, the Netherlands, Finland, etc.), which demonstrate a high level of professional training of teachers in accordance with world standards; preserve the rich historical traditions of education, which ensures their leading role in the scientific and educational field; have accumulated significant experience of training teachers in new socio-cultural conditions present particular interest. The professional training of future teachers in these countries is used as a flexible element of professional and personal development of a person throughout life. The article identifies the main trends in the training of future teachers in Germany, France, Canada, Denmark, the Netherlands, Finland and others in the context of their personal and professional development and its adaptation to the conditions of professional training of future teachers in the domestic educational space of higher education institutions. Foreign experience in the training of future teachers has opened up new opportunities to improve the system of professional development of future teachers in Ukraine.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".