Chinese MBA Students’ Perceptions of Business English Writing: Needs Analysis and Student Self-Reflections
Bibliographic record
Abstract
Despite the steady growth in the recruitment and education of MBA students in China, there is a dearth of research on MBA students’ perceptions of Business English Writing (BEW) in this context. This paper conducts a qualitative inquiry into Chinese MBA students’ perceptions of BEW in English as a foreign language context in China. Forty-four MBA students of a ten-week BEW course participated in this study. An open-ended questionnaire was used near the end of the course to elicit their work-related writing needs, self-reflections on BEW abilities, and perceptions of the BEW course. A focus group was conducted with six students to provide insights into the students’ work-related writing experience. The results showed that the students’ work-related writing needs differed in terms of their respective job positions, with those working in foreign-funded enterprises or joint ventures having more job-related demands to write in English than those working at state-owned enterprises. Moreover, the students generally regarded their BEW abilities as moderately good or low, with distinct expectations of the BEW course raised. Pedagogic implications were drawn for improving BEW course in the Chinese context.
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.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".