COVID-19: Ensuring Continuity of Learning During Scholastic Disruption in Tertiary Institutions in Nigeria
Bibliographic record
Abstract
Issues concerning learning during educational disruption due to the Covid-19 pandemic have been the subject of many excellent journalistic accounts, but there has not been much scholarly output addressing the experience. The need to maintain social distance poses a significant challenge to the international communities particularly between populations, educators and students. Though elicited by COVID-19 pandemic, the focal point of this challenge remains how to offer learning opportunities to students while stakeholders make efforts to contain an awfully virulent pandemic. In Europe and elsewhere, technology has helped with distance learning; assisting individuals on the margins of society and those in formal economy to achieve learning objectives despite a compulsory social distance regime. In other areas of the world such as Africa, correlation between technology and affordability has become a new frontier for continuing education. Encumbrances brought about by COVID-19 have deeply subverted education, state security, sociopolitical stability and economic development, which in turn create or preserve untoward anomaly. In this light, Africa has become the ground zero of disorientation where disorganized criminal groups fester due to poor education and fewer opportunities. The article examines the effect of COVID-19 in the continuing tertiary education relations and concludes that while blended learning is conceivable in Nigeria, rural schools might not benefit from the programme due to truncated development in communication and low level of technology. The use of affordable Internet Radio is thus, recommended for Nigeria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".