The Impact of the War in Ukraine on the Emotional well-being of Students in the Learning Process
Bibliographic record
Abstract
Background: The research is devoted to the current problem – the emotional well-being of students in learning in the war conditions in Ukraine. Objective: To study the state of the emotional well-being of students in the process of learning during the war, to identify the factors of emotional well-being in the learning process, and to determine the ways to ensure it in the conditions of war. Methods: А questionnaire developed by the authors of the article and a method of assessing mental activation, interest, emotional tone, tension, and comfort (L. Kurgansky T. Nemchyn). The study's results made it possible to find out the impact of the war on the emotional well-being of students and compare the state of the emotional well-being of students during education in peacetime and during the war. Indicators of interest in learning and comfort decreased, and indicators of emotional tension and mental activation increased in students. It is established that the emotional well-being of students in the learning process depends on the nature of the pedagogical interaction, the ability of the teacher to create a situation of success for each student, the level of anxiety of students in the process of including them in educational activities, and the characteristics of the relationship with classmates. Conclusions: The observed negative trends in the emotional well-being of students during the war became the basis for determining the methods of correctional work in order to help children cope with the experience of war.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".