Psychological Microclimate of Student Groups, Studying in Different Instructional Formats
Bibliographic record
Abstract
The article discusses psychological microclimate of a student group under different instructional formats (teaching and learning face-to-face, online, or blended). The main objective of the study was to explore factors that contribute the microclimate formation in connection with the changes that the formal postsecondary education undergoes because of the COVID-19 pandemic. One hundred and sixty-six students of several higher education institutions in Rostov-on-Don, Russia took part in the study. To fulfill the study objective, data about various individual characteristics of participating students and their subjective rating of the psychological microclimate in the respective student groups were collected by means of psychological testing and subjected first to the ANOVA and then to the multiple regression analyses. ANOVA revealed no statistically significant differences across instructional formats either in the microclimate scores or in the respondents’ psychological characteristics. The follow-up multiple regression analysis explored models of joint contribution of the predictor variables to the formation of the microclimate in student groups. Specifically, it found that the relationship between the microclimate and emotional tone is negative, whereas generosity and leadership inclinations are positive predictors of the psychological microclimate in student groups. Better understanding what factors determine dynamic interactions among students in various instructional formats could be instrumental in optimizing microclimate in students group, undoubtedly affected by rather dramatic changes in all aspects of our social lives, caused by the current epidemiological situation in the world. Psychological microclimate in a group is, in turn, capable of seriously impacting on students’ learning performance and psychological wellbeing.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".