A Narrative Case History of Distance Education Before, During, and After COVID-19 in China and Iran
Bibliographic record
Abstract
Educational hub refers to centres of excellence in higher education and research whose aims are to provide high-quality education for both national and international students to enhance the competitiveness of the country. These educational hubs provide an opportunity for knowledge exchanges and innovation in local regions through education and training. In response to the COVID-19 pandemic, rapid shifts were made towards online learning in education around the world. Although the lockdown is over, remote learning will likely play an increasingly prominent role in education. The adoption of scaled remote learning during the pandemic provided evidence of the importance of online learning. They offer an insight into global society, helping prepare students for an increasingly interconnected world by facilitating links between different regions. Educational hubs can be tied to distance learning and are successful in attracting international students when offering a combination of distance learning methods and innovative programs. This paper examines the phenomenon of educational hubs in higher education for international education through online learning with digital technology. New opportunities for online and distance learning within the definition of educational hubs are analyzed, and three online and blended learning models that reflect the development of educational hubs based on COVID-19 conditions of education are offered. In addition, the successful cases and experiences of distance learning hubs in China and Iran in recent years are described.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".