Social Adaptation of Students in a Multicultural Environment during Distance Learning
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
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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