Implementation of Blended Learning Rotation Model in Teaching Business English and Business Ukrainian in Higher Education Institutions
Bibliographic record
Abstract
The article is devoted to the problem of implementation of a blended learning approach in the language training of undergraduate students specializing in International Economic Relations and Public Management and Administration. The historical background, structural and functional features of blended learning are outlined. The relevance of the study is determined by the benefits of combining online and offline learning modes at Ukrainian universities in wartime as well as by the absence of specialized scientific works providing linguistic and methodological support for interdisciplinary teaching of Business English and Business Ukrainian. Based on the ideas of foreign and Ukrainian scientists and modern methods of scientific research, the present experimental study proves the effectiveness of the rotation model of blended learning for the acquisition of systematized linguistic knowledge, skills, and abilities needed for the effective use of the native and foreign languages in professional communication. The article is illustrated with tables, figures, and samples of instructional materials placed on the Moodle online platform. It also outlines the perspectives for future research on other aspects of blended language learning and interdisciplinary teaching at the university.
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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 | 0.003 | 0.003 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".