Introduction of New Forms of Education in Modern Higher and Vocational Education and Training
Bibliographic record
Abstract
This article sets sights on highlighting the effectiveness and efficiency of higher and vocational education and training, as well as exploring ways to address and implement the current reform agenda in the field. The research was conducted on the basis of a generalizing and comparative method, to identify the problems and development of vocational and higher education. Within the framework of the conducted research the current state of vocational and higher education has been characterized; the features of online learning at leading universities and its advantages has been clarified; the prospects of introduction of continuity of education have been studied, for the development of personality abilities, taking into account changes in society in the context of improvement of the system of vocational and higher education caused by the European integration process of education; directions for the development of vocational and higher education as part of the national education system and society in general have been outlined. It is determined, that at the present stage the domestic education system should be improved and transferred to an innovative way of development in accordance with developed countries. In the near future, such modern forms of education as: distance education, dual education, continuing vocational education and others, should be improved and implemented into the educational process.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".