The COVID-19 pandemic: a catalyst for creativity and collaboration for online learning and work-based higher education systems and processes
Bibliographic record
Abstract
Purpose The purpose of this research is to focus on work-based problems catalysed by the COVID-19 global pandemic, based on a case study of a multi-continental, multi-campus university distributed across Kenya, Tanzania, Uganda and Pakistan. Higher education institutions (HEIs) in developing countries lacked pre-existing infrastructure to support online education and/or policy and regulatory frameworks during the pandemic. The university's programmes in Pakistan and East Africa provide lessons to other developing countries' HEIs. The university's focus on teaching and learning and staff development has had a transformational organisational effect. Design/methodology/approach Case study with participatory approaches aimed at co-production of responsive systems and co-creation of effective curriculum and faculty training is used. Findings Systems and processes developed across the university in the effort to ensure educational continuity. From the disruption to all educational programmes and the disarray of regulatory bodies' responses, collaboration emerged as a key driver of positive change. The findings reiterate the value of trust and provision of opportunities for those with the requisite competencies to lead in a participatory and distributive manner whilst addressing limited human and financial resources. The findings reflect on previous work respecting organisational change recast in the digital age. Originality/value This paper reflects the authors' work in real-time as they led and managed changes encountered during the COVID-19 pandemic. The paper will be of value to management and leadership cadres, particularly in developing contexts, responsible for recovery and sustainability of the higher education sector.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".