Experience of implementation of STEM-education in the USA and Canada
Bibliographic record
Abstract
With the development of digital technologies, globalization of society and fundamental changes in all branches of the economy began to take place at a rapid pace. To support the country's economic development, specialists are required, equipped with modern knowledge and ability to navigate and develop in a changing world of technology. The provision of the labor market by highly skilled professionals entirely depends on educational policy. But the decline of young people's interest in scientific and engineering activities is a significant obstacle to this process. Therefore, there is a problem of popularization and introduction of innovation activity in the educational process. The transition to new technologies should become an integral part of the learning process of future professionals, competitive in the labor market. Ukraine is now on the path to reforming the educational sector. The success of reform depends entirely on its support in society and in particular by teachers. Therefore, there is a need to learn the experience of teaching and supporting teachers, finding ways to systematically and successfully implement reforms, and the need to study the experience of countries that have already gone this way, with advanced technology and leadership in other countries. Studying the experience of changes in education and in the formation of the professional community of teachers in the United States and Canada, will help identify the most progressive and effective systems, tools and methods for preparing future teachers for professional activities. Using the USA and Canada as an example, modern approaches to the standardization of STEM distribution processes in educational practices are analyzed, documents defining the requirements for STEM implementation in teacher education programs, their place in education, are reviewed. Key words: STEM-education, STEM-learning, education reform, interdisciplinarity, pre- and in-service teachers, certification, standards, post-secondary education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".