THE ROLE OF SOFT SKILLS IN TEACHER TRAINING IN THE MODERN EDUCATIONAL PROCESS
Bibliographic record
Abstract
In the article, the authors analyse the concept of ‘soft skills’ and define the role of soft skills in the professional training of teachers in the modern educational process. It is established that these skills include the ability to empathise, active listening, constructive communication, conflict management and leadership qualities that contribute to the effectiveness of the teaching process, stimulate active interaction between participants in the educational process and contribute to the successful achievement of educational and professional goals. It is noted that developed soft skills allow a teacher to more effectively establish interaction with students, colleagues and the administration of a higher education institution. The ability to communicate, motivate and manage emotions creates an atmosphere of mutual understanding and trust, which has a positive impact on the success of students and forms their positive attitude to learning in general. In the article, the authors analyse the experience of other countries in developing soft skills for teachers, in particular in the Scandinavian countries, Singapore, Canada, Australia, and Japan. The study found that in order to develop soft skills in teachers, it is necessary to attend trainings and seminars, which will help teachers improve their dialogue skills, better understand the needs of students to resolve conflict situations and create a positive educational environment; create conditions for the professional growth of teachers, recognition of their achievements; encourage teachers to share experiences and cooperate with colleagues, which will help improve their teamwork and conflict resolution skills.
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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 | 0.003 | 0.006 |
| 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 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".