Higher Education Challenges in the Era of Covid-19, from the Perspective of Educators and Students (Ghana, Georgia and Pakistan Cases) – A literature Review
Bibliographic record
Abstract
For the last three years, the entire world has faced a colossal phenomenon - the covid-19 pandemic. All sectors and areas of life have been affected, forcing rapid and radical changes towards adaptation in its wake. Inevitably, the unexpected pandemic’s mark and impact on education is more severe and longer lasting than imagined. It disrupted education provision at an unprecedented scale. This article is intended as a review of literature on the experience of different countries and education systems during the Covid-19 pandemic. Based on the analysis of the existing literature and research on this issue, from the perspective of educators and students, including the experience of different countries around the world, the pandemic has had a great impact on higher education and pushed it to digital transformation, implicitly overcoming important challenges. The review uses particular examples of higher education in the era of Covid-19 in Georgia, Ghana and Pakistan, exposing measures taken to continue educating in spite of the pandemic. However challenging this phenomenon proved to be, it equally gave way to enormous opportunities for creativity within progress. Discussed are barriers that students and academics faced during online teaching-learning, the pros and cons of online teaching-learning, as well as the quality of teaching-learning and the state of preparedness for future education.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".