Development of Distance Learning in the Context of Covid-19
Bibliographic record
Abstract
According to a UNESCO report, the main factor in the disruption of the education system in the 21st century was the quarantine measures of the COVID-19 pandemic, which directly affected the education of more than 220 million students in the world (UNESCO, 2021). Thus, the purpose of the study is to assess the level of education of the graduates of higher education in Great Britain from May - November 2021 during quarantine measures. The achievement of the set goal was implemented through a survey of 1157 students from various higher education institutions in Great Britain, which was conducted in May and November 2021. This made it possible to identify certain regularities and trends in the adaptation of the English system of higher education to new conditions of the study. Thus, self-study and distance learning under the supervision of teachers became the determining method of education, which in percentage terms reached 55%, and at the same time, the level of group work in studying previously presented lecture material decreased by 36% (from 76% to 40%) due to technical difficulties and physical stay students in different parts of the country. However, the overwhelming majority of students remained motivated to study and showed adaptation to the new online educational environment. Overall, the study highlights the importance of developing and supporting distance learning in the future, which can become an additional tool to ensure access to education worldwide.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".