To Teach or Not to Teach: An International Study of Language Teachers’ Experiences of Online Teaching During the COVID-19 Pandemic
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
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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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