Exploring The Factors Affecting Classroom Participation in The Saudi EFL Virtual Learning Classrooms During Covid-19 Pandemi
Bibliographic record
Abstract
During the COVID-19 pandemic, the sudden shift from face-to-face to virtual learning classrooms impacted students’ performance in the classroom, including English as a Foreign Language instruction in Saudi Arabia. Since students’ learning depends upon their participation in physical classroom activities and discussions, the present study focuses on understanding the factors that influence the participation of King Abdul Aziz University preparatory year students in the full virtual learning environment. Following a quantitative research approach, an online questionnaire with 165 participants was utilized for data collection and statistically analyzed using SPSS software. The analyzed data indicated that eight factors strongly influenced students’ participation: virtual learning environment, learning environment at home, teacher, grades, class activities, internet, instructional support and feedback, and social lockdown. The findings provide valuable insights for EFL instructors who wish to adapt their instructional approaches to create an engaging virtual learning environment that encourages active participation through class discussions and activities. The study offers recommendations to improve participation in the virtual learning environment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".