Post pandemic Era: English Language Teachers’ Perspectives on Using the Madrasati E-Learning Platform in Saudi Arabian Secondary and Intermediate Schools
Bibliographic record
Abstract
Despite numerous studies on the sudden need to switch from conventional classroom-based education to e-learning during the COVID-19 pandemic, general agreement on the method’s efficacy, advantages, disadvantages, challenges, and opportunities has not yet been reached. Investigating the perspectives of a wide range of teachers on this subject is therefore important. This study investigates the perspectives of English language teachers who use the Madrasati online teaching platform in secondary and intermediate schools. Its data was gathered via a questionnaire survey which was distributed to 24 male and female teachers. The findings showed that, while most teachers’ initial response to online learning was negative, over time, their views became more positive. The teachers reported that the Madrasati platform built pupils’ independence and provided major advantages to the educational system. It made marking homework faster and more efficient and facilitated communication with school administrators and pupils’ parents and helped the personal development of teachers and pupils. The study found that the Madrasati platform provided opportunities for self-education, learner autonomy, and acquiring English outside the conventional face-to-face classrooms which can be built upon.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".