The Problem of Methodological Training of Future Teachers in the Digital Environment
Bibliographic record
Abstract
Changes in modern socio-economic conditions and modernization of the global and Ukrainian education system impose new requirements for education policy, which should meet the progressive needs of the state and society. Given the changing paradigm of education, society and the state urgently need to prepare applicants for higher education - future teaching specialists for professional activities in the educational digital environment. The study aims to identify the factors affecting the quality of methodological training of future elementary school teachers in the modern educational environment. Methodology. To identify the factors influencing the effectiveness of methodological training of future elementary school teachers in the digital environment, the method of theoretical analysis was applied.To determine the advantages and disadvantages of distance learning, the method of comparative analysis of the elements of classroom and distance learning was used. Results. The theoretical and methodological analysis of the problems of teacher training in the conditions of digitalization allowed to identify the factors and obstacles affecting the effectiveness of methodological training of future elementary school teachers. Conclusions. The study of the problem of methodological training of future teachers in the conditions of digitalization in Ukraine and abroad allowed to identify gaps in the professional training of elementary school teachers today. The results of this theoretical study can serve as the basis for further research (theoretical and empirical) in the field of professional training of future teachers.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".