PREPARING TEACHERS OF VOCATIONAL EDUCATION INSTITUTIONS TO DEVELOP STUDENTS' PERSONALITIES IN TODAY'S WORLD
Bibliographic record
Abstract
DOI: https://doi.org/10.26565/2074-8922-2025-85-26 Purpose. Justification of theoretical and methodological foundations, analysis of current trends, and identification of ways to improve the training of vocational education teachers for work aimed at developing the personalities of vocational education students. Methods. Analysis of scientific literature, comparative-analytical method, generalization of pedagogical experience. Results. The article discusses the theoretical and methodological foundations and practical aspects of training teachers in vocational education institutions to develop the personalities of students in the context of modern social transformations. It focuses on the changing role of the teacher—from imparting knowledge to becoming a mentor, facilitator, and moderator of the educational process, capable of creating conditions for the comprehensive development of students. It analyzes contemporary scientific approaches to understanding the essence of personal development and identifies its key components (professional, cognitive, social-communicative, value-moral, and personal-psychological). The results of a comparative analysis of domestic and foreign experience in teacher training, in particular the practices of Germany, Finland, Canada, Singapore, Poland, and France, are presented. The main problems of the modern system of teacher training are identified: insufficient integration of psychological, pedagogical, and methodological knowledge, limited development of soft skills, low level of digital competence, and weak cooperation with employers. The following areas for improvement in teacher training are proposed: the introduction of competency-based, person-oriented, activity-based, innovative-technological, and reflective-research approaches; expansion of the practical component of education; and the formation of a system of continuous professional development. It is substantiated that effective training of teaching staff is a key condition for the formation of competitive, socially active, and spiritually mature specialists in vocational education. Conclusions. Effective training of teaching staff at vocational education institutions for the personal development of students involves the integration of professional, psychological, pedagogical, and digital competencies, the introduction of innovative technologies, and the formation of humanistic values. This is a necessary condition for training competitive, responsible, and creative specialists in modern vocational education. In cites: Bozhko N. V. (2025). Preparing teachers of vocational education institutions to develop students' personalities in today's world. Problems of Engineering Pedagogic Education, (85), 310-323. https://doi.org/10.26565/2074-8922-2025-85-26 (in Ukrainian)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".