Understanding and Enhancing Academic Experiences of Culturally and Linguistically-Diverse International Students in Canada
Bibliographic record
Abstract
Canada is among the top four most popular destinations for international students. Using narrative inquiry, this chapter explores lived experiences of two international students. The study is guided by three broad questions: 1) What are the main positive experiences of international students in Canada? 2) What are some of the challenges faced by international students during their studies in Canada? and 3) What should be done to enhance academic success of international students? Data were collected using semi-structured interviews as conversations. Data analysis reveals three main themes: mismatch between the students' academic expectations and reality, challenges relating to language, and self-efficacy and resilience. Recommendations are presented. These include working with international students to help identify factors that are likely to enhance the success of international students as identified by the students so that universities can create campus environments that allow for success.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".