STEM-ПІДХІД В ОСВІТІ ТА ПІДГОТОВКА ВЧИТЕЛЯ ДО ЙОГО ВПРОВАДЖЕННЯ
Bibliographic record
Abstract
The article considers the essence and potential of STEM-education, the implementation of which is important for the training of specialists of the new generation and socio-economic development of our country. The core of the basic concepts of STEM-education as a new direction in education is revealed, the appearance of the acronym "STEM" is covered, its meaning is discussed, and the conceptual field of its synonymous categories is analyzed. The main advantages of this area and the main issues of STEM-approach implementation in the domestic education system are clarified. The experience of the world's leading countries (including the USA, Great Britain, and Canada) in the implementation and development of STEM-education is analyzed. The conclusion about efficiency of activity of the establishments of the general secondary education focused on STEM-technologies is made. The necessity of improving the education system of Ukraine focusing on the experience of STEM programs implementation in the above-mentioned countries is substantiated. Possibilities of application of STEM-technologies in the New Ukrainian School (NUS) are defined (at an initial stage of training in particular). The conclusion is made about the need to restructure the whole learning process, which presupposes moving away from the teacher-centered model of learning, giving students initiative, stimulating their activity, activating development of critical thinking and creativity. It is noted that the role of teachers in STEM-education is changing, which, in turn, raises the issue of training and retraining of teachers who could work in this direction and transfer the process of STEM-education from individual to mass. The need to provide educational institutions with appropriate material resources (LEGO designing sets, computers, new generation of textbooks, etc.) has been confirmed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".