A Trend Analysis of the Challenges of International Students Over 21 Years
Bibliographic record
Abstract
International students leave their countries to pursue their educational goals in a different country and must adapt to succeed. However, they may face challenges when adapting to and learning a new culture. This study investigates the challenges common to international students in their host countries and summarizes the publishing trends. A literature search of peer-reviewed articles published in Scopus, Taylor & Francis, EBSCO Host, Web of Science, Springer, PubMed, and Wiley Online over 21 years (2002–2022) was done for data collection. After the screening, a total of 175 articles were included in this review and analyzed with content analysis. The findings show that the top four destinations for international students (USA, UK, Australia, and Canada) produced the most articles about international students’ challenges. Additionally, most papers investigated more than one challenge, and sociocultural (82.9%) and academic challenges (82.3%) were the most researched, with language issues as the primary cause. The results also show no changes or improvement in the challenges of international students in 21 years, and areas such as psychological and economic challenges need more research. These challenges and other trends found in the articles are discussed and directions for future research are suggested.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".