Emergency Remote Teaching in Higher Education During Covid-19: Challenges and Opportunities
Bibliographic record
Abstract
The third quarter of 2020 marks the closure of on-campus face-to-face pedagogies in South Africa’s institutions of higher learning due to Coronavirus disease (COVID-19). The need to maintain social distancing necessitated the transition to emergency remote teaching. A few institutions of higher learnings could move their classes effectively to online and distance education platforms because of their pre-existing experience and some grapple with managing the ‘new normal’. This article reflects on the challenges and opportunities of an emergency remote teaching in institutions of higher learnings during the COVID-19 pandemic. The article adopted a qualitative approach through relevant literature and policy reviews to critically analyse emergency remote teaching during the era of COVID-19. The findings indicate that some staff and students experience challenges related to the lack of resources and exposure to remotely use information and communication technology. The article acclaims that institutions of higher learnings should acquire suitable information and communication technology equipment and develop the requisite facilities, implement rules and regulations for their availability, and adequate maintenance. This recommends promoting technologically compliant ethics within the institution, provide easy access to teaching and learning by both students and academic staff at an affordable and fixed (secure) cost in safe, conducive, and unrestricted environments for students.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".