Implementing Content-Based Instruction in Online ESP Course within the System of Professional Training of Future Officers
Bibliographic record
Abstract
The article examines the capabilities of content-based instruction (CBI) within the system of professional training of future officers of the State Border Guard Service of Ukraine, specifically within the online English for Specific Purposes (ESP) course. The authors argue that nowadays, due to the quarantine restrictions and ongoing war initiated by Russia, it is crucial to enhance the border guards’ foreign language competence through a distance learning system. The study showed positive results in applying the CBI strategy to deliver an ESP course. This approach contributes immensely to developing context-appropriate language competence, boosts motivation-driven engagement, and increases retention and long-term academic success rates. The course content includes such topics as intercultural communication, illicit trafficking of radiological and nuclear materials, human trafficking, and fundamental rights. To deliver the content of the CBI course, the authors had to consider its online format and work out such learning activities as reading and listening to authentic job-related content, completing online interactive activities, and engaging in case-studying and problem-solving activities. The course results showed a considerable improvement in learners’ ability to effectively communicate in English within the professional border guard context, use the foreign language to build knowledge and skills around human values, recognise, analyse, and solve various border-related incidents involving topics covered in the course. The effectiveness of the online ESP course studied based on CBI has shown that implementing this approach in online education deserves recognition and acceptance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".