Conflicting Student Viewpoints about Online EFL Learning during the Covid-19 Pandemic: Focus on Effective Learning and Well-being
Bibliographic record
Abstract
Due to the total shutdown caused by the COVID-19 pandemic, worldwide countries regardless of their development shifted instruction to the online learning method. This study aims to investigate the views of students in undergraduate studies about the impact of online (EFL) English foreign language learning on educational issues and well-being. Through questionnaires and focus group discussions, participants revealed their real sensitivities and views about online EFL learning, thus both quantitative and qualitative research methods are implemented in the present study. Their experiences showed the advantages that supported their needs during a certain period such as the pandemic, but also the challenges they went through during that time. The study presents social and educational implications that are directly related to technology and environmental conditions during online learning that affected the well-being of students. Results showed that shifting to online classes uncovered contradictory preferences among students regarding online learning and teaching. Finally, this study provides an overview, that in case the implementation of online or hybrid learning is requested again in the future, the necessary preparations that meet the educational and social needs of the students should be taken into account.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".