Understanding the choice of Chinese graduate students’ English language learning in a Canadian post-secondary context
Bibliographic record
Abstract
This study explores the English learning strategies employed by Chinese graduate students before and after their arrival in Canada, to document and analyze shifts in strategic approaches to language acquisition in a specific context of post-secondary internationalization.Narrative inquiry data collection methods, framed by a qualitative co-constructivist theoretical paradigm, shaped the research.Themes emerging from data analysis suggested that participants' reliance on memory and cognitive strategies employed in China were abandoned in favor of utilization of compensation and social strategies after moving to Canada.Participants made these changes primarily because of their desire to make best use o f the English-speaking Canadian environment to improve English proficiency, as an explicit and significant aspect of their personal motivations for internationalization.An important finding of this research is that immersion in the English-speaking environment inspired participants to employ different English learning strategies leading to significant improvement in English proficiency.The study seeks to address a gap in the research literature on internationalization and second language acquisition, in order to understand and mitigate the well-documented challenges experienced by international students in transitioning from one linguistic context to a dramatically different one.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".