Understanding Chinese International Students’ Experiences in the U.S. Higher Education through the Trans Constructs of Transnationalism, Transculturalism, and Translanguaging
Bibliographic record
Abstract
Although the United States has a long history of welcoming students from around the world, current political discourse questions the wisdom of these programs. This qualitative research study explores how Chinese international students negotiate their lived experiences in U.S. universities through the lenses of transnationalism, transculturalism, and translanguaging. Interviews were conducted with 25 informants to qualitatively examine their experiences in higher education institutions in the West; the authors gained insights into how they navigated these aspects of their lives and shaped their complex identities. The multidimensional analysis provides an in-depth view that clarifies misconceptions about the opportunities and challenges these students encounter in U.S. universities. The conclusions reveal that Chinese international students lead complex, multifaceted lives shaped by global mobility and local academic expectations. Their stories reflect negotiations across language, cultures, and national boundaries. Yet many of their struggles remain invisible due to systemic myths and institutional unpreparedness. Greater understanding of their lived experiences can (a) guide institutional policy, (b) challenge myths and deficit narratives, and (c) promote inclusive, globally aware learning environments. This can also help reframe identity as multidimensional, improving educational offerings for all students and educators in higher education institutions.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".