The Effectiveness of Flipped Classroom in English Language Learning: A Meta-Analysis
Bibliographic record
Abstract
The Flipped Classroom (FC) model, a teaching method used in various educational settings, including language learning, aims to improve student engagement and understanding. Its application in English language learning involves restructuring traditional teaching and learning methods. This study was meticulously designed to assess FC's effectiveness in improving English language proficiency. A comprehensive meta-analysis was conducted on research articles from January 2021 to November 2023, retrieved from ERIC and the Scopus Index. After a rigorous independent review and data extraction process by two reviewers, nine studies with a total of 705 participants were included. The methodological quality of the selected articles was evaluated using the Fail-Safe N for Publication Bias Assessment. The results, which showed that FC was more effective than conventional methods in enhancing overall English language proficiency (SMD=0.85, 95% CI -0.57 to 1.12, P<.001, I2=65.45%), knowledge (SMD=0.84, 95% CI -0.55 to 1.12, P<.001, I2=49.49%), and skills (SMD=0.70, 95% CI -0.30 to 1.11, P<.01, I2=75.97%), instill confidence in the robustness of our findings. These results suggest that FC has the potential to significantly improve English language acquisition outcomes. However, further research with larger sample sizes is needed to confirm and strengthen these results.
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 | 0.032 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.052 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".