Season of Migration to Remote Language Learning Platforms: Voices from EFL University Learners
Bibliographic record
Abstract
The abrupt disruption of the traditional face-to-face language instruction due to the unprecedented COVID-19 pandemic has forced many schools and higher learning institutions in Oman and around the globe to establish a virtual learning environment. This crisis-prompted remote learning has been a new experience for most teachers and students alike, a variable that may affect students' learning. Thus, it is significant to understand the students' experience with online teaching and learning. This study explicitly examines online teaching and learning as perceived by English as a Foreign Language (EFL) students of a higher learning institution in Oman. A total number of (112) undergraduate students in Oman acted as a data source by responding to a computer-assisted survey questionnaire. The survey focused on the following themes: overall first-time online language learning experience; online courses; online learning mode and attainment of graduate attributes; effectiveness of online teaching and delivery; utilization and usefulness of electronic learning devices; and e-learning language skills. The findings highlight the significance of exploring learners' online learning experience and its implications for planning, implementing, teaching, and assessing online language education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".