The Formation of Key Competences in the Training Process of Preschool Education Specialists (Experience of the United Kingdom, Germany, Ukraine)
Bibliographic record
Abstract
The article outlines the main prerequisites for the transformation of modern competences in the training process of preschool education specialists (experience of the United Kingdom, Germany, Ukraine). Differences in the training of future preschool education specialists are found in the structure and content of educational programs (curriculums), namely: the bachelor’s training of preschool teachers is applied in Germany, as well as master’s programs for training of preschool education specialists; the development of new forms and methods of teaching occupies an important place in advancing of professional teacher education in the UK; they make it possible to create the future teachers’ ability for independent and critical thinking, the practical application of acquired knowledge and experience in educational research, role-based and simulation modeling, and creative search. In terms of professional competences, the most advanced training system of preschool teachers is revealed in the UK. However, in Germany, the process of training specialists in preschool education area was determined by institutional decisions, where preschool education was a form of childcare and part of social security. The United Kingdom is characterized by a longstanding tradition of a detailed, comprehensive framework of teacher’s competences for training of preschool teachers. Therefore, one of the core competences is professionalism and standards in combination with such competences, as: values, knowledge and skills. As opposed to Germany and the United Kingdom, in Ukraine the formation of key competences in the process of training specialists in preschool education is primarily based on professional basic knowledge and skills, values and attitudes, motives of pedagogical activity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".