CHALLENGES FOR CHINESE INTERNATIONAL UNIVERSITY GRADUATE STUDENTS IN CANADIAN ANGLOPHONE CLASSROOMS
Bibliographic record
Abstract
As globalization is affecting all aspects of civilization in the first decade of the 21st century, global higher education is on the cusp of a transformation period. University students from Mainland China encounter a great many challenges in cross-cultural communications while studying in Western countries like Canada. This thesis explores and analyzes the challenges that Chinese international university graduate students (CIUGSs) at Master’s level are encountering in Canadian Anglophone classrooms by identifying the challenges, causes of these challenges and the impacts of these challenges. I address the above three research questions through the lens of a multi-method qualitative approach that includes participant observation, individual interviews and a study of my own experiences. To answer the above three research questions, the six-dimensions of culture (Hofstede, Hofstede, & Minkov, 2010) and the theory of capital (Bourdieu,1986) are employed to analyze the findings. The findings of this study identify that different features of the six-dimensions of culture between Canada and Mainland China cause considerable challenges for CIUGSs in participating in class activities. Furthermore, this study aligns with five of the six-dimensions but does not agree with the judgment of Hofstede et al. (2010), which categorized Chinese culture as having weak uncertainty avoidance. Additionally, it is asserted that the lack of a decent quality of various forms of Canadian capital generates difficulties for CIUGSs and may engender inequity in Canadian classrooms. This study indicates that Chinese features in the six-dimensions of culture engender obstacles for CIUGSs in obtaining Canadian capitals. To better participate in cross-cultural communications in Canadian classrooms, CIUGSs have to obtain sufficient quality of Canadian forms of capital, in this way, their Canadian habitus is cultivated gradually. Eventually their cultural attributes in the six-dimensions can be altered to approach Canadian style and, as a result, their performance in class activities can be enhanced. To better help CIUGSs to improve their performance in Canadian university Anglophone classrooms, suggestions for CIUGSs, instructors and university administrators are provided in this 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".