Preparing Future Teachers for the Development of Students' Emotional Intelligence
Bibliographic record
Abstract
Objective: This study deals with the issues of preparing future educators for the development of students' emotional intelligence. The study aims to identify the most important aspects of future teachers training in the framework of this task, to identify the main factors that should be paid attention to when training teachers, whose tasks in the future will include the development of emotional intelligence of school students. Background: Particular attention is paid to the study of the training of teaching staff for the development of emotional intelligence in students with psychological disturbances. The object of the study is the branches of pedagogy of higher education aimed at educating qualified teaching staff that can effectively solve the tasks of developing the emotional intelligence of elementary school students. Method: In the study, methods of system analysis of the subject matter are used, based on a detailed examination of the studies of the material available for research, with a qualitative analysis of all the collected information. Results: Based on the conducted studies were formed recommendations on the training of teachers for future practical activities within the framework of the issues under consideration, which are summarised as follows: in training, attention should be paid to working with educational games, the task of which is to develop students' skills in identifying and evaluating emotions, their own and of students participating in the game, the study and analysis of works of classical literature, as a method of learning to think, analyse and develop the emotional sphere, training in the implementation of special exercises for the development of the emotional sphere. Conclusion: The study has important applied value in terms of expanding the understanding of emotional intelligence and the need for its development, starting from the lower grades of high school.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".