The overview of open education development tendencies in Russia and Canada
Bibliographic record
Abstract
The aim of research is to describe the foreign experience of open educational resources implementation in higher education on the example of Canada. The concept of open education in this country requires federal and regional support from the government. Currently, there is a number of initiatives, aimed at maximizing the development of open educational resources. An increasing number of open educational resource initiatives in Canadian universities offer free courses to both Canadians and international students. National open education initiatives include the following projects: “Open Data”, the federal government “pilot” project; support of Canadian Council of Ministers of Education within 2012 Paris Open Educational Resources Declaration; and the Creative Society of Canada. British Columbia and Alberta support open educational resources as a part of the most important open education initiatives at the regional level. However, a number of problems prevent the full development of the open education system. Materials and methods of research include discourse-analysis of domestic and foreign scientific sources, devoted to the problems of open education. The methodological basis is the study of the legal framework of open education, a retrospective analysis of the open education development process in Canadian universities and the analysis of existing open educational projects. The results of the study show that the need to implement the policy of open education imposes new requirements to the process of determining the level of educational programs development and the formation of a unified system of formal credits. Conclusion. The Canadian examination of open educational resources regulates all sectors of education and training in the country. This examination is a review of the results of open education principles implementation at the national and regional levels. The article discusses the features of both regional and foreign projects functioning. The advantages of open educational policy for different segments of the population are described.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".