Why Don’t They Participate? A Self-Study of Chinese Graduate Students’ Classroom:Involvement in North America
Bibliographic record
Abstract
China is now the world’s largest source of international students. In terms of learning performance, Chinese graduate students studying in North America exhibit distinct differences from students who are born and raised in North America. Conflicting cultural values compel Chinese students to reconcile East-West cultures, and put an onus on North American instructors to implement culturally-sensitive pedagogy. Employing the theoretic framework of yin-yang theory, this paper examines Chinese graduate students’ classroom performance against the backdrop of East-West cultural negotiation, and specifically seeks to identify which factors inhibit Chinese graduate students’ participation in North American classrooms. Drawing from their own living experiences, the authors employ self-study in the methodological form of narrative inquiry – in conjunction with references from existing literature – to investigate Chinese graduates’ classroom challenges. Results reveal six factors impacting students’ classroom performance: language; knowledge of the education system; knowledge of the social system; personality; influence of traditional culture; and social/economic/political changes. Future research directions are also suggested.Key words: Chinese graduates, East-West, cross-culture, North America, classroom involvement, self-study
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".