Digitalization of vocational education under crisis conditions
Bibliographic record
Abstract
The rapid development of technologies and their application in all branches of the economy calls for digitalization of education as a prerequisite of improving the quality of vocational training. Digital technologies in their turn allow to diversify the mode of training according to the needs arising under various circumstances. In some countries like Australia and Canada, online and blended learning are the only possibly form of training due to learners’ remotedness to schools. But as recent experience shows, introduction of online education was the only way out to sustain it under the conditions of the COVID-19 and now by the wartime and absence of access to educational facilities. In this was, the necessity of digitalization of education is constantly growing together with its increasing range of applicability. Now all production processes and processes of the service sector are under the influence of digital technologies, because modern machines are operated by computers. Modern military equipment is also digitally based and operated. Thus, working in modern industries and services requires a high level of digital literacy, which presents a challenge for the system of vocational education. Under modern conditions, irrespective of their positive or negative origin, vocational schools (VS) should be ready to train specialists for various spheres of industry capable of working with constantly changing digital technologies. This fact puts forwards certain requirements to digital literacy of both students and teachers, who have to cooperate through digital devices and software to attain the set educational goals. All these circumstances require the equal level of digital literacy of both teachers and students to provide educational institutions with the latest material base and digital resources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".