A systematic review of international students’ experiences transitioning from non-Anglophone high schools to universities in Anglophone settings
Bibliographic record
Abstract
This systematic review explores the extent and nature of research on the experiences of international students transitioning from non-anglophone high schools to universities in anglophone settings and then curates and synthesises the empirical evidence on their experiences. An extensive and systematic search of the literature revealed 1247 potentially eligible papers. After screening, eleven empirical studies were deemed suitable for data extraction for narrative synthesis focusing on students’ transitional experiences. The university destinations in the included studies were the USA (n = 8), Canada (n = 2) and a variety of English-speaking university destinations (n = 1). Seven of the included studies were cross-sectional, and four studies were longitudinal. Results showed that previous studies have explored students’ decision-making processes, university and international readiness, and language and identity development. Five studies suggested the need for longitudinal perspectives to better understand student participants’ transition. Four studies proposed to incorporate the perspectives and impact of relevant stakeholders in investigating students’ transitional experiences, including students’ families, teachers, peers, and university professors. This review calls for more targeted, rigorous, and longitudinal studies to better understand and enhance international students’ experiences studying abroad.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".