The Lived Experiences of Asian International Students in the U.S. Higher Education
Bibliographic record
Abstract
Asian international students have long constituted the largest portion of the international student body in the United States (U.S.), a trend that persists despite the hurdles presented by the COVID-19 pandemic. These students play an essential role in stimulating the U.S. economy, facilitating cross-cultural exchange, and nurturing international collaboration and understanding. During the 2022-2023 academic year, Asian international students comprised 70.3% of the total international student population in the U.S. higher education. The number of Asian international students in the U.S. higher education institutions is so considerable that their lived experiences within the U.S. higher education system deserves a thorough examination. Existing empirical research demonstrates that plenty of Asian international students are underserved, mainly facing culture shock, linguistic barriers, racial discrimination, and mental health issues. This systematic literature review aims to: 1. Investigate whether Asian international students face any challenges in the U.S. higher education; 2. Classify the challenges (if any) encountered by Asian international students in the U.S. higher education; 3. Offer valuable insights to key stakeholders in international education, empowering them to refine current administrative policies and teaching pedagogies to best support the well-being and success of Asian international students in the U.S. higher education.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".