Global Perceptions of Faculties on Virtual Programme Delivery and Assessment in Higher Education Institutions During the 2020 COVID-19 Pandemic
Bibliographic record
Abstract
Amidst the outbreak of COVID-19 worldwide, virtually all national governments declared a “lockdown” of all institutions in a bid to curtail its spread. This posed serious challenges to programme delivery and assessment in Higher Education Institutions (HEIs), with foreseeable long and short-term consequences. This study investigated the effectiveness of virtual programme delivery and assessment in Higher Education Institutions (HEIs) during the COVID-19 (Corona Virus) pandemic, from a global perspective. The study assesses the success rate of virtual teaching and learning via various online platforms that were set up to make up for time lost due to the unanticipated global HEIs closure. Organisational Change Theory was used to inform the study, within the confines of simple qualitative research approach. Data were collected using interview while participants were selected through convenience sampling technique via online platforms such as the reputable online academic community, email, WhatsApp, and the UNESCO website. Data were analysed using thematic analysis. The findings revealed disparities in responses to virtual learning across HEIs and national contexts. Training and re-training of lecturers and students, and the provision of virtual learning enabling infrastructure, were recommended to mitigate similar situation in future.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".