Academic, Linguistic, and Socio-Cultural Experiences in the Acculturation of Chinese International Graduate Students
Bibliographic record
Abstract
An increasing number of graduate students are choosing to pursue their studies internationally and Canada is one of the principal host countries for these students, particularly students from China. The push by many Canadian universities to increase their foreign enrollment has led to new classroom dynamics. Chinese international students (CIS) are at the centre of this change. However, getting accepted into a Canadian graduate program does not necessarily mean that the challenges facing these students are over. Often, they encounter social, cultural, linguistic, and academic challenges that as they navigate the unfamiliar environments they find themselves in. The purpose of this study is to use a conceptual framework based on the notion of acculturation to explore these challenges faced by four Chinese international students who have completed their Master’s of Education degrees in Second Language Education. My goal is not only to provide a platform to highlight the voices of these students, but also to create a greater understanding of the challenges they face for the edification of their classmates, professors and university administrators. The findings of this study demonstrate that my respondents encountered a variety of challenges during their graduate studies in Canada, both inside and outside of the classroom. Although numerous studies have been conducted that examine various aspects of the experiences of international students, none have used the Seidman (2013) three-interview qualitative method. This method provides opportunities for prolonged lengths of time to be spent with each participant, which therefore allows for greater depth of investigation to be reached with each. This study demonstrates how a sample of Chinese international students met and dealt with the socio-cultural, linguistic, and more particularly academic challenges they encountered in Canadian graduate-level courses.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".