Online Learning and Teaching Experiences During the COVID-19 Pandemic: A Case Study of Bangladeshi Students Receiving China’s Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it