Building Learning Communities in Saudi EFL Online Classes
Bibliographic record
Abstract
The transition from on-campus to online classes during Covid-19 pandemic has emphasised the role of online learning. Therefore, teachers need to adjust their teaching strategies to help students interact in this new learning environment. One of the methods that help to engage students in online classes is building learning communities. The aim of the present study is to explore the various strategies that teachers can implement to build learning communities in Saudi EFL online classes and investigate students’ perceptions of the use of these strategies. The researchers collected data in two stages. The first stage involved conducting interviews with 11 EFL teachers. The second stage involved a questionnaire that was based on the teachers’ interview responses and was utilized to obtain the perception on teachers’ strategies from 275 EFL students. The results indicate that EFL teachers implement various strategies that promote teacher-student interaction, student-student interaction, and student-content interaction. Moreover, the results show that EFL students have a positive attitude towards the implementation of these strategies in the context of Saudi EFL online classes. It is suggested that learning communities could be developed through the implementation of teaching strategies that promote classroom interaction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".