Post pandemic Era: English Language Teachers’ Perspectives on Using the Madrasati E-Learning Platform in Saudi Arabian Secondary and Intermediate Schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it