Difficulties in Teaching English for Specific Purposes: Empirical Study at Vietnam Universities
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
<p>In recent years, teaching English, especially English for specific purposes at Vietnam universities has received a lot of attention from students, teachers, and relevant authorities because of not high teaching effectiveness. This results in the fact that students after graduation do not meet English requirements of employers, so unemployment becomes more serious. This is an alarming situation because English is becoming the almost indispensable communication language of young people nowadays. This empirical study consists of a survey of teachers and students at universities in Hanoi by listing the factors related to teaching English for specific purposes. Then, we give some recommendations for improving effectiveness of teaching English for specific purposes so that students can meet the English requirements for their work and lives.</p><p><br /><strong></strong></p>
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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