To Teach or Not to Teach: An International Study of Language Teachers’ Experiences of Online Teaching During the COVID-19 Pandemic
Bibliographic record
Abstract
Schools have been switching to online learning to ensure students' learning continuity during the COVID-19 pandemic. However, there is a paucity of studies examining language teachers' motivations and decisions for continuing online teaching in the future. This study aims at investigating the significant factors influencing language teachers' motivations and decisions on online teaching. Based on the aim of this study, three research questions guided this study: (1) What was language teachers' experience of online teaching? (2) What motivates language teachers to teach online after the COVID-19 pandemic? (3) What demotivates language teachers to teach online after the COVID-19 pandemic? Eight language teachers coming from six countries and regions, namely, Australia, Canada, Hong Kong, New Zealand, Russia, and Taiwan, were selected to have two one-on-one semi-structured interviews. The researcher used Social Cognitive Career Theory as a theoretical framework and Interpretative Phenomenological Analysis as the methodology to examine language teachers' experiences in-depth. This study found that better time management and a positive learning environment are the reasons for continuing online language teaching, while personal beliefs on education and negative teaching outcome expectations are the reasons for stopping online language teaching. The findings can provide insights for the education institutions, school management and policy-makers to devise appropriate strategies to boost language teachers' motivations to incorporate online teaching in the post-pandemic era.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".