Integrating Content and Language in Business English Teaching in China: First Year Students’ Perceptions and Learning Experience
Bibliographic record
Abstract
Content and language integrated learning (CLIL) is a key construct in Business English teaching in universities in China today. While there is a plethora of articles on implementation in European contexts, there is limited evidence in the literature of the teaching/learning experience in other foreign language learning environments—despite its wide application in, for example, south-east Asia and China in particular. As CLIL programs have been developed in a variety of ways to meet the unique needs of learners and societal expectations, the context of teaching and learning is critical. This paper focuses on the perceptions and learning experiences of students in a first year, first semester course, Introduction to Contemporary Business, in a Chinese university. Lesson observations, questionnaires, and interviews explore the experience of learners. While most students found the course very challenging in their first semester, they met the challenge. Coping with both language and content is always a double challenge: most students found their Introduction to Contemporary Business their most difficult course, yet they perceived it as manageable and worthwhile. Students coped with the difficulty level in two main ways: either by spending much time in review and translating the textbook prior to class, or by focusing on the teacher’s PowerPoint slides after class—as they considered these were the key points and the textbook was too difficult. Suggestions for a closer integration between language and content within CLIL courses are offered, such as a case-task-based approach, a greater variety of input, and the role of content teachers in English enhancement.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".