Emergency remote teaching of English as a foreign language during COVID-19: Perspectives from a university in China
Bibliographic record
Abstract
Given the circumstances of the global pandemic, universities around China and across the globe have suspended face to face (F2F) classes and transitioned to emergency remote teaching (ERT). University students in China have been the first to go through the whole semester’s ERT including College English, a compulsory language course for almost all the first- and second-year students of non-English majors. This article adopted a mixed-methods design, a survey followed by a qualitative visual method, gathered data on students’ experience about ERT of College English and presented an investigation into detailed interactive process of the classes. The data analysis on the learners’ engagement and the feedback from the learners provided a summary of the key threads of ERT classes. This study demonstrated that students held an extrinsic goal orientation, which did not differ from their face-to-face learning experience. ERT granted students more opportunities for interaction with their instructor and peers, while collaboration among students were limited. The research results can be connected to the larger fabric of global language teaching in crisis context, provide empirical lessons to educators, and help instructors with their future decision-making about technology-supported activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".