Study of the Emotional Well-Being of Students in the Process of Education in the Modern School
Bibliographic record
Abstract
The article is devoted to the current problem of the modern school - the emotional well-being of students in the learning process. The study analyzes the role of the emotional component in students' learning activities, the impact of emotions on learning outcomes, and the importance of emotional well-being in maintaining and strengthening their health. The purpose of the study was to identify the causes of emotional discomfort of students in the learning process, the ways to ensure the emotional well-being of students, the implementation of which will improve learning success, maintain and strengthen students' health. The following methods were used to study the state of the emotional well-being of the students: 1) a questionnaire developed by the authors of the research to study the emotional well-being of students at school; 2) the method of assessment of mental activation, interest, emotional tone, tension and comfort, created by L. Kurganskyi and his colleagues; 3) a card to identify factors of learning success. The results of the study showed a low level of the emotional well-being of students in modern schools. Based on the analysis of the study outcomes, the causes of emotional discomfort of students in the learning process are identified. The possibility of improving the effectiveness of learning, educational achievements of students by ensuring their emotional well-being in the learning process is described. Practical ways to improve the emotional well-being of students in the modern school and the formation of their emotional health are outlined and substantiated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".