An Empirical Study on Business English Teaching and Development in China—A Needs Analysis Approach
Bibliographic record
Abstract
This paper first reviews the developmental history and status quo of Business English Program in China. Then based on the theory of needs analysis, it researches on 226 questionnaires from Business English Program students from Guangdong University of Foreign Studies to investigate the problems encountered and current situation of Business English Program in China. From the statistical analysis of the questionnaires, it finds: (1) employment and interest are the main reasons that students choose Business English as their major, but the current Business English teaching materials haven’t fully considered student’s demands for vocational requirements; (2) it should take into account both the learners’ and societies’ needs in Business English curriculum arrangement to increase the number of business-related courses appropriately; (3) students generally agreed that they lacks business knowledge and skills, so Business English courses should focus on cultivating the practical business skills; (4) Business English teaching materials should always be practicality oriented and targeted and communicative; (5) in order to improve teaching effectiveness, Business English teachers should consciously adopt different teaching methods targeted for different teaching content during the teaching process; (6) teachers’ morality, knowledge, teaching style and mutual relationship with students can be further enhanced to meet the needs of students.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".