Simulation-Based Learning as an Effective Method of Practical Training of Future Translators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The research topicality is determined by the problem of lack of qualified specialists who have a high level of foreign language proficiency and the ability to carry out effective professional foreign language communication. The study involved the following methods: Rokich’s Value Orientations Test, Nemov’s methods for diagnosing the expectation of success level, the Self-Efficacy Scale (R. Schwarzer, M. Jerusalem); testing on the material taught on the Theory and Practice of English Translation, chi-squared test, Mann-Whitney U test. Results: Simulation of real conditions and situations of translation activity is used in almost every lesson (80%), promoting the development of future translators’ professional competencies. The final control in the experimental group found that all students had a high (48.10%) or medium (51.30%) level of foreign language proficiency, which confirms the effectiveness of the simulation method. In the experimental group, the percentage of students with a low level of foreign language proficiency at the end of the research decreased from 26.3% to 0.6%, and the percentage of students with a high level of foreign language proficiency almost tripled. At the same time, in the control group the number of students with a low level of foreign language proficiency decreased from 25% to 10%, while the percentage of students with a high level of foreign language proficiency increased by only 1.6 times. Therefore, the hypothesis of this scientific research was experimentally confirmed. Simulation training promotes the development of foreign language competencies of students majoring in Translation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it