The Effect of Flipped Classroom Instruction on Developing Saudi EFL Learners’ Comprehension of Conversational Implicatures
Bibliographic record
Abstract
While pragmatic instruction has received considerable attention from researchers of interlanguage pragmatics over the last three decades, its effective implementation in the EFL classroom remains an unresolved question. The flipped classroom model is a recently developed teaching method that constitutes a role change for teachers and learners, inverting the front-of-class instruction paradigm in favor of active and collaborative classroom learning. To potentially take advantage of this promising trend, the present study seeks to explore the effectiveness of the flipped classroom for developing Saudi EFL undergraduates’ pragmatic competence and language proficiency by focusing on the comprehension of conversational implicatures during one academic semester. A total of 100 students, assigned to flipped teaching group (n=50) and traditional teaching group (n=50), participated in the study. To elicit the required data, the Oxford Placement Test, a discourse completion test, and reflective e-portfolios were used. A post-test revealed that pragmatic competence significantly increased in the case of the flipped group. The mean score of the flipped group (M=18.48) was considerably higher than that of the traditional group (M=14.68). In following the flipped model of instruction, this progress was influenced by effective out-of-class preparation and appropriate manipulation of in-class time.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".