The Reality of Active Learning Application in Jeddah Schools by English Teachers
Bibliographic record
Abstract
This study explored the extent to which teachers of English as a Foreign Language (EFL) adopt and implement active learning strategies in their classrooms. The study, also, examined the obstacles encountering high school teachers to use active learning and it delved further into exploring the participants’ recommendations to mitigate these obstacles. The study followed a quantitative methodology. Sixty-six EFL teachers (Male n=22 and Female n=44) from Jeddah in the Kingdom of Saudi Arabia participated in this study. The researchers used an electronic custom-designed, 19-items rated on a five-point Likert scale questionnaire, for ease of dissemination and data collection. The findings revealed that the degree of employing active learning was medium (54.8%). However, 55% of the participants responded that they encountered some acute obstacles to implementing active learning in their classrooms. The findings showed no statistically significant differences attributed to gender, experience, and training about using active learning, obstacles, and recommendations to overcome the obstacles. The study discusses some of the obstacles to implementing active learning and concludes with some recommendations to the Ministry of Education to reinforce active learning in the education system of EFL contexts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".