The Role of Competencies in the Educational Process of Training Future Specialists in the “Labour Training and Technologies” Speciality
Bibliographic record
Abstract
From a theoretical point of view, Ukrainian education for teachers of the "Labour training and technologies" specialty is in the process of searching for effective methods that would ensure the future education of schoolchildren through the transfer of relevant competencies, which are regulated by education standards. Such competencies have been analyzed in the present research. In addition, the theoretical developments of both domestic and foreign teachers, in particular, of Scandinavian countries, are taken into account in the formation of labor education for schoolchildren. The present research pays particular attention to the newest standpoint in the assessment of the perception of competencies and their understanding by students of the "Labour Training and Technologies" specialty. The academic paper represents the results of acquiring the competencies by students of the "Labour training and technologies" specialty at the Central Ukrainian State Pedagogical University named after Volodymyr Vynnychenko. The main revealed regularities indicate the fact that students of the corresponding specialty quite superficially understand the role and significance of mastering the competencies defined by the standard of Ukrainian education. A characteristic feature of acquiring the competencies by students is their awareness of civic and social competencies. Competencies in the field of natural sciences, engineering and technology, and innovation are perceived by them to a lower rank. The research has identified the most painful problems regarding competencies that are not perceived by students, namely: creative activity, information, a communication component, and culture. Insufficient attention is paid to mathematical competence, financial literacy, and the environmental component. The results of the present research have indicated the need for additional discussions on the methods of perception of competencies by students to improve the educational process and curricula towards increasing learning efficiency. The research results obtained create opportunities for a more flexible expansion of applying existing digital systems in the formation of competencies according to educational standards and the implementation of a motivational approach in increasing the level of self-development of students and teachers of the "Labour Training and Technologies" specialty.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".