COVID-19 pandemic providing a window of opportunity for higher education: Case study of a three-country teaching-learning experience
Bibliographic record
Abstract
Aim: Since March 2020, the COVID-19 pandemic has been causing unprecedented challenges to higher education by disrupting traditional face-to-face teaching as well as international mobility of students, faculty and staff. The factual knock-out of established modes of teaching and learning and the restriction of international travel called for rapid action and a shift towards remote learning and teaching. Methods: Within the framework of a pragmatic approach, global health faculty from Fulda University of Applied Sciences in Germany and York University in Canada, including a small group of public health students from Cluj in Romania, established a globally networked learning environment. Between November and December 2020, a total of 147 students participated in joint virtual lectures and international collaborative group projects. To capture the acceptance and effectiveness of the innovative didactic experience, a semi-structured student survey was conducted directly after the last session. Results: The overall rating of internet-based cross-university teaching-learning was positive: Students reported benefits of an enriched learning experience through the sharing of different perspectives, approaches and debates with international professors and peers. Success and overcoming challenges for collaboration among students depended strongly on the level of coordination relating to time differences and expectations. Conclusion: The COVID-19 pandemic has revealed that transnational inter-university teaching-learning is feasible, provides a beneficial pedagogic option and points promising ways to the future. Conflict of interest: None declared. Acknowledgements: We gratefully acknowledge the contributions of Prof. Dr. Kai Michelsen and Prof. Dr. Marius I. Ungureanu to the development of the three-country teaching-learning experience.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".