Ensuring sustainable development through the use of digital educational hubs for teaching civic education at school
Bibliographic record
Abstract
Abstract The article highlights the practical experience of implementing digital educational hubs to support civic education (CE) to ensure sustainable development. The role of the digital educational hub as a modern tool of the digital educational environment was determined. The digital educational hub is a multi-functional digital educational environment with electronic resources, tools, technologies and educational materials where organisational and pedagogical conditions are created for the acquisition of knowledge, the development of students’ competencies and the effective accumulation of intellectual potential, which contributes to professional development and the implementation of innovations in practice. The purpose of this article is to investigate how digital educational hubs support civic education at school, how teachers and school leaders can use the resources of the hubs, and how teachers in Ukraine and other countries are supported in using digital tools for teaching civic education subjects, which are important for the professional development of teachers, including the development of their digital skills. The experience of using digital educational hubs supporting CE at schools in Ukraine and European countries (Belgium, Canada, Netherlands) is described. The practical significance of the digital educational hub, as a component of the digital educational environment, is to support and implement innovations in school education that contribute to the development of the personality of a modern citizen, an active member of the digital society. The novelty of the research: online learning and remote forms of communication are the most popular in education today. For teaching civic education, there is a combination of active forms of learning with the use of digital means and resources, which are actively offered by digital educational hubs. Highlighting the best practices of such use is important both for the professional development of teachers and for the diversification and activation of students’ civic education. The practical significance based on the experience of European countries, forms and approaches to the use of digital educational tools for teaching civic education has been identified. Methods of using digital hubs to support civic education at school need further research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".