Using Professionally-Oriented Electronic Educational Resources to Enhance Foreign Language Competence of Future Border Guard Officers
Bibliographic record
Abstract
Nowadays, in the context of the restrictions imposed by pandemic and war initiated by Russian federation, it became indispensable to make adjustments to the educational process and use relevant methods to teach future border guard officers of Ukraine foreign languages in order to maximize the efficiency of the educational process at the higher military educational institution. The results of the analysis of practical experience, as well as taking into account the best international practices for training security and defense sector officers, made it possible to determine the optimal electronic educational technologies that can be used for foreign language training of future officers in the context of a various restriction to the educational process. To test the effectiveness of integrating electronic educational resources into the training of future officers, four training groups of the National Academy of the State Border Guard Service were involved, who studied at the same course in the specialty "State Border Security" and "Telecommunications and Radio Engineering". Professional training in the experimental group was carried out with the maximum possible application of electronic educational resources. The effectiveness of integrating professionally-oriented training programs with interface in English, as well as specifically designed ESP distance learning course into the system of professional training of future officers has been proved in practice. This was confirmed by the results of diagnostics of academic achievements of cadets who used electronic means for professional English language training. Although, it is recommended to adhering to a certain ratio of the share of classes using electronic educational resources and practical contact classes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".