How Chinese Exchange Students Adapt to Their Academic Course Learningin a US University: A Fresh Look at College English Teaching in China
Bibliographic record
Abstract
This paper aims to depict the linguistic challenges that Sino-US exchange students face when they adapt to the demands of English-medium higher education in the US and learning strategies that they came up with to overcome the obstacles in their pursuit of academic learning via in-depth interviews and questionnaire. These findings are complemented by data collected from the real chats, classroom observations, and field notes of over 100 exchange students in a US university. The evidence shows that these students have been tided over the linguistic problems by a combination of learning strategies, strong motivation, diligence, collaborative efforts and resort to reference in Chinese for academic assistance. To probe into the transition period from mainly Chinese-medium courses to those conducted solely in English medium that they have experienced, this article reveals a “thick description” of how thirty exchange Chinese students adapt themselves to English-medium courses by tracking, describing and probing into influences exerted by the exchange program with the aim to revaluate the current College English curriculum prevailing in most universities or colleges in China.
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 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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".