Ubiquitous e‐Teaching and e‐Learning: China’s Massive Adoption of Online Education and Launching MOOCs Internationally during the COVID‐19 Outbreak
Bibliographic record
Abstract
China had made a remarkable headway in online education provision during the first quarter of 2020 due to the coronavirus disease 2019 (COVID‐19) outbreak, a global public health crisis that acted as a catalyst for the uptake in online education as a method for students’ e‐learning and teachers’ e‐teaching at a vast number of institutions worldwide. China’s launching of XuetangX Global and iCourse International , two massive online open course (MOOC) platforms in April 2020 to provide distant e‐learning solutions to global learners at a time they were most needed, proves to be a timely move as the global challenge caused by this pandemic turned out to be an opportunity in disguise for online education internationally. This article centers around China’s opportune development in online education and launching university MOOCs internationally in the height of the worsening COVID‐19 pandemic in early 2020 and examines its preparedness, implementation, and impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".