Challenges and Strategies for Online Learning and Teaching during COVID-19 in Indonesia and Afghanistan
Bibliographic record
Abstract
The significant impact of the pandemic has altered many sectors of life, including higher education. The COVID-19 outbreak has created outnumbering challenges for students and lecturers, forcing them to adjust to online learning and teaching. They develop specific strategies to tackle the challenges and study as normally as possible. In this regard, the study investigates the challenges students and lecturers face during COVID-19 online learning and teaching at a private university in Afghanistan and a public university in Indonesia. Furthermore, it explores the strategies they applied during COVID-19 online learning and teaching to deal with these challenges. In addition, it is intended to compare the students' and lecturers' experiences with online learning and teaching in both countries. In order to obtain the data, the study employs open-ended questionnaires using Google Forms. The Google Form is distributed through WhatsApp and emails to students and lecturers at a public university in Indonesia and a private university in Afghanistan. Data analysis uses the online engagement framework for higher education to filter and generate themes into concepts. The study found that during COVID-19, both Indonesian and Afghan students and lecturers faced several challenges, yet the strategies they applied differed according to each country's social and development context. Identifying the challenges and the strategy of online teaching and learning provides practical understanding for students, lecturers, and universities.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".