Training of Future Teachers for Work with Preschoolers under Martial Law in Ukraine
Bibliographic record
Abstract
The article is aimed to check the effectiveness of the formation of professional competencies of future specialists in preschool education facilities under martial law. A structural-functional model was chosen as an experimental model of competence formation. Methods. The paper used interviewing and testing techniques, the method of expert analysis of students’ educational achievements. To identify the psychological characteristics of students’ educational motivation, we used the method for studying the motivation of learning at a university by T. I. Ilyina. The Welch's t-test and Student's t-test were used to comparing the levels. The results. Professional motives began to dominate in the majority of students (24%) after the end of the first stage of the experiment and the number of students who have formed professional competences increased significantly at the end of the experiment (4.8). It has been proven that the motivational-value component (2,8) plays a key role in the structure of the student’s professional competence. The results of the experiment allow us to conclude improvement of the levels of formation of future educators’ professional competence. The creative component showed special dynamics (58%). Conclusions. Despite the state of war, the structural-functional model demonstrates positive results in the formation of professional competencies. Prospects for further research should focus on the effective methods of organizing the educational process in wartime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".