Social Adaptation of Students in a Multicultural Environment during Distance Learning
Bibliographic record
Abstract
The problem of social adaptation of students in a multicultural environment has always been a concern of pedagogical science. With the development of telecommunication technologies, educational institutions face with the issue of finding effective forms of interaction between students. The aim of the article is to theoretically and empirically develop an integrated concept of studying the resilience of foreign students in a multicultural environment during distance learning. Methods: survey, methods of remote collection and processing of information (GOOGLE Forms), Self-Determination Test; K. Riff’s Scale of Psychological Well-Being, statistical methods of data processing, methods of analysis of the reliability of survey. The correlation between adaptation and resilience of foreign students who participated in the study is (r = 0.45; p <0.01). In turn, the resilience index is 81.92%. According to the study, students show high and medium rates of resilience during distance learning in the process of adaptation to a multicultural environment. It has been found that most students do not have difficulty adapting to distance learning due to the coronavirus pandemic (COVID-19). Thus, it was found that the process of adaptation of students in a multicultural environment during distance learning allowed maintaining a high rate of resilience, which indicates its effectiveness. Further research should be aimed at studying the development of professional competencies among students of narrow educational and professional training. It is also necessary to develop in detail the methodology for implementing the model of adaptation to distance learning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".