Understanding Teachers’ Integration of Moodle in EFL Classrooms: A Case Study
Bibliographic record
Abstract
The study explores the integration and implementation of the Moodle platform at the English Language Center of the Salalah College of Technology. To achieve this purpose, a qualitative, interpretive approach with a case study research design was used to collect the data and to deepen our understanding of the phenomena and how it was constructed in social reality of the school.Two teachers have been chosen to be the interviewees, to give their opinions and views on the topic under study, and the factors affecting both the implementation and integration of the Moodle programme. It was evident from the narratives of the two interviewees that the integration of Moodle was successful, and that it has proven to be a useful tool in the teaching and learning processes of English. In spite of some existing factors that may hinder the working mechanisms of the implementation and integration of Moodle, it may be concluded that this platform could be recommended to be extended to the other skills of the English language that it currently does not support. Following this process will inevitably improve the comprehension and production of the English language and related materials, online and real, respectively.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".