The Flipped Classroom Impact in Grammar Class on EFL Saudi Secondary School Students’ Performances and Attitudes
Bibliographic record
Abstract
The aim of this study was to apply the flipped classroom strategy in teaching English grammar to examine its impact on secondary school students’ performances, perceptions, and attitudes toward learning English independently. The researcher implemented the flipped classroom strategy by selecting videos based on the students’ textbook and uploading those videos on the Edmodo site before each lesson to provide opportunities for active learning interactions. The students of the experimental group (n = 20) were required to watch the videos to learn by themselves and to come to class prepared to ask for clarification, if needed. They also practiced what they had learned under the teacher’s supervision by completing collaborative and competitive tasks in groups or pairs. Meanwhile, the control group students (n = 23) received in-class only traditional teaching. They learned the grammatical lessons without the help of any videos. The statistical analysis of the post-test results showed that adopting the flipped classroom strategy appeared to play a role in enhancing the students’ grammar performances, as the mean score of the experimental group was higher than that of the control group, but this difference was not statistically significant. The students’ responses to a questionnaire and semi-structured interviews indicated that their attitudes’ towards using the flipped classroom strategy in the EFL class were positive.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".