Teaching and Learning in Higher Education in Bangladesh during the COVID-19 Pandemic: Learning from the Challenges
Bibliographic record
Abstract
The higher education sector globally has gone through a transition because of the coronavirus outbreak, and as a result, many traditional higher education institutions across the globe have been forced to go online to provide education and arrange assessments so that their students could continue their education and complete their courses. Unlike developed countries, at the beginning of the lockdown, most of the higher education institutions in Bangladesh shut down their operations, and a few universities started moving toward online distance teaching and learning activities. Based on an empirical study, this article discusses the challenges of teaching and learning in higher education in Bangladesh during the COVID-19 lockdown. It also identifies good practices to overcome those challenges. An online survey was conducted to collect data from university teachers throughout the country. Findings from this study show that it was a great challenge for most universities to adopt online teaching and learning models at the beginning of the pandemic. Many factors, such as preparedness, limited resources including financial means, low digital literacy, internet connectivity and suitable physical and virtual infrastructure affected this transition. However, the findings also show that the COVID-19 pandemic created new opportunities for educators and practitioners to explore various professional development activities by trying out different digital pedagogies through practice and reflection. This article also highlights the immediate effect and long-term impact on teaching and learning regarding preparedness for future approaches to education in emergencies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".