Modern Tools for Increasing the Effectiveness of Distance Education in the Conditions of Digitalization
Bibliographic record
Abstract
The current stage of developing digital technologies creates favorable conditions for intensifying the improvement of innovative distance education tools, the need for which is due to the intensification of the latest challenges and dangers. The research purpose is to substantiate the theoretical and applied principles of identifying the influence of modern tools for increasing the effectiveness of distance education in the conditions of digitalization. The methodological basis of the research includes general scientific and special methods of economic analysis and fundamental scientific investigation, in particular: system analysis, synthesis, scientific abstraction, comparison, analogy, statistical analysis, cluster analysis (k-means method), tabular, graphic, generalization, and systematization. The research results have revealed that the effectiveness of distance education in the conditions of digitalization significantly depends on the country’s development level. It has been found that three groups stand out among European countries, characterized by different levels of digitization and quality of distance education: highly developed countries (Denmark (MID: 0,90–1,00), Estonia (MID: 0,78–0,83), Ireland (MID: 0,76–0,80), Luxembourg (MID: 0,79–1,00), the Netherlands (MID: 0,91–0,95), Germany (MID: 0,81–0,88), Finland (MID: 0,88–0,90), Sweden (MID: 0,83–0,88); countries with an intermediate level of development (Bulgaria (MID: 0,58–0,62), Spain (MID: 0,62–0,64), Cyprus (MID: 0,64–0,67), Lithuania (MID: 0,63–0,66), Malta (MID: 0,64–0,70), Poland (MID: 0,54–0,64), Portugal (MID: 0,61–0,64), Romania (MID: 0,54–0,60), France (MID: 0,73–0,73), the Czech Republic (MID: 0,61–0,64), Slovenia (MID: 0,63–0,65), Azerbaijan (MID: 0,59–0,63) and countries with a low level of development (Greece (MID: 0,48–0,51), Italy (MID: 0,55–0,58), Latvia (MID: 0,55–0,59), Hungary (MID: 0,47–0,52), Slovakia (MID: 0,51–0,55), Croatia (MID: 0,46–0,50), Armenia (MID: 0,47–0,56), Georgia (MID: 0,48–0,50), Moldova (MID: 0,42–0,48), Ukraine (MID: 0,41–0,84). It has been proven that the most common digital tools for increasing the effectiveness of distance education are Viber (86,7%), an educational platform determined by the educational institution (60%), YouTube lessons (39,3%), Skype (13,3%) and Facebook (6%). It is proposed to increase the effectiveness of distance education by deepening society digitalization in countries with a low level of development and providing them with methodological assistance on the part of highly developed countries.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".