Online Learning and Teaching Experiences During the COVID-19 Pandemic: A Case Study of Bangladeshi Students Receiving China’s Higher Education
Bibliographic record
Abstract
While facing the COVID-19 pandemic attack worldwide, international students are forced to turn to online instruction for academic study. Based on a longitudinal ethnography with a cohort of Bangladeshi students who study in English-medium degree program at software engineering, this study reveals a series of challenges confronting both Chinese teachers and Bangladeshi students for their online interactions. Data were collected through online classroom observation, semi-structured interviews, audio-recording and online interactions. From the perspective of Chinese teachers, they lacked of control on their students’ class participation given the poor network infrastructure in Bangladesh and the time gap between China and Bangladesh; in terms of Bangladeshi students, they felt frustrated in access to Chinese-mediated online teaching applications due to their insufficient Chinese proficiency; their inaccessibility to operate their subject learning also made the online learning tedious. Based on the findings, the study offers several suggestions to respond to teachers and students’ difficulties and challenges in online lessons and sheds some lights on improving online education.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".