Chinese Graduate Students at North American Universities: Learning Challenges and Coping Strategies
Bibliographic record
Abstract
The paper investigated four Chinese graduate students’ perceptions of the challenges they face and the coping strategies they use in their English academic learning at two North American universities. They reported experiencing the following seven major challenges in their academic learning: 1) financial difficulties; 2) problems in using English for academic purposes; 3) frustrations in becoming a permanent resident; 4) difficulty in adapting to the classroom learning environment; 5) lack of critical thinking skills; 6) acculturation problems; and 7) loneliness and academic anxiety. For each of the seven challenges they have developed corresponding coping strategies. Cet article examine la perception des défis auxquels devaient faire face quatre étudiants chinois et les stratégies qu’ils employaient pour survivre dans le milieu d’apprentissage académique anglais de deux universités en Amérique du nord. Ils ont éprouvé, disaient-ils dans leurs récits, les sept défis importants suivants: 1) des difficultés financières; 2) des problèmes sur l’usage de l’anglais pour des fins académiques; 3) des frustrations dans leurs applications pour la résidence permanente; 4) des difficultés d’adaptation au milieu d’apprentissage de leurs classes; 5) un manque d'aptitude d’esprit crituque; 6) des problèmes d’acculturation; et 7) la solitude et l'anxiété académique. Pour chacun de ces défis, ils ont développé des stratégies de survie correspondantes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| 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".