The Impact of Covid-19 on International Higher Education: New Models for the New Normal
Bibliographic record
Abstract
COVID-19 has had a major impact on international higher education with border closures, cancelled flights, and a shift to online teaching and learning. As a result, many international students have decided to either abandon or defer their plans to study abroad. If students stay in their home countries, many institutions that rely heavily on foreign students’ fees will suffer, with potential impacts on national economies. Beyond the economic implications, it is also important to consider the personal impact of COVID-19 on international students, who may face delays or obstacles to program completion, employment and/or immigration. Though there are certainly risks and losses in the short term, the demand for international education, and the benefits it offers, are expected to grow. This presents an opportunity for higher education institutions (HEIs) and governments, not just to lessen the impact of COVID-19 on their current business models, but to explore new models and opportunities. HEIs and governments must look at redefining international higher education for the new normal, which will entail a shift in policies and programmes. This paper outlines the implications of the COVID-19 crisis for international higher education and presents potential opportunities for governments and higher education institutions to refresh and redefine their approaches for the new normal.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.029 | 0.021 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".