THE ROLE OF SOFT SKILLS IN TEACHER TRAINING IN THE MODERN EDUCATIONAL PROCESS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it