Digital Platforms in a Distance Learning Environment: An Educational Trend or the Need of the Hour?
Bibliographic record
Abstract
The COVID-19 pandemic has demonstrated the promise of distance education using digital technologies. For Ukraine, this method of education has gained additional importance, since after the beginning of the unprecedented for the 21st century. Russian military aggression, the appeal to distance education technologies has become relevant. The purpose of the article is to analyze digital distance learning platforms, determine their effectiveness and prospects for further use. The work uses general scientific research methods (analysis, synthesis, induction, deduction). At the same time, the research is based on the principles of generalization, specification and abstraction. With the help of the comparison method, it was possible to compare the use of digital distance learning platforms in Ukraine, the USA and Saudi Arabia, where specific empirical data are presented. The research also used the axiomatic method, which provides for the ascent from theoretical statements (axioms) to specific conclusions. The conclusions concluded that distance learning is cheaper and more productive because it leaves more time for students to improve themselves and implement their own projects, and for teachers to conduct scientific work. The example of Saudi Arabia shows that a large number of students can be reached through the use of digital technologies and distance learning. This experience is useful for Ukraine. It is recommended to develop digital technologies of distance education in Ukraine, because its use will make it possible to save budget funds, guaranteeing the maximum involvement of teachers and students in the educational space.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".