Distance Learning During COVID-19: EFL Students’ Engagement and Motivation from Teachers’ Perspectives
Bibliographic record
Abstract
The present mixed-method study aimed to explore 114 female secondary-level English teachers’ perceptions of the effectiveness of distance education in public schools in the Kingdom of Saudi Arabia during COVID-19 using a Google Forms questionnaire. Additionally, the challenges teachers faced in distance education and their attitudes toward teacher-training programs during the pandemic were investigated in semi-structured interviews. The findings indicated an overall positive view toward the effectiveness of distance education. However, the interviewed participants expressed their preference for traditional in-class teaching due to their familiarity with it compared to distance teaching. Online classes gave students the opportunity to become more actively engaged. Distance education was also found to promote students’, especially shy students, motivation for learning and participating in class activities. Teachers indicated some challenges in distance teaching, such as a lack of internet connection and human interaction, technical issues, assessment reliability, increased workload, and students’ unwillingness to learn. Finally, recommendations for more effective distance education were provided, namely, technological and pedagogical training for teachers, the need for technical support, and proper training for students on online learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".