Impacts of COVID-19 on International Students in the U.S
Bibliographic record
Abstract
COVID -19 is a recent pandemic that has affected all sectors of the economy, including higher education. The magnitude of the pandemic in the education sector has been diverse, with many disruptions being evidenced. The pandemic has particularly disrupted learning across the world due to the closure of schools. The international students have been adversely affected owing to their precarious situation. This literature review study explored how COVID 19 affected international students in the US. The study identified that the closure of on-site educational instructions coupled with international travel restrictions left international students in precarious situations where they were not learning but the international students could not travel back home. This had trickle-down negative effects on their finances/budget and emotions. The move to e-learning put the international students at a disadvantage as it amplified inequality in the education sector, Based on these devastating impacts, the study recommends the need for policy and practice changes to protect international students from such devastating impacts in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".