Exploring Flipped Classrooms in EFL Teaching: A Comprehensive Systematic Review
Bibliographic record
Abstract
The flipped classroom model has gained significant attention as an innovative pedagogical approach to teaching English as a Foreign Language (EFL). This method promotes active learning, student engagement, and improved language proficiency by shifting content delivery to pre-class activities and dedicating class time to interactive and collaborative tasks. This systematic review synthesizes findings from seven peer-reviewed studies published between 2016 and 2024, selected through a PRISMA-guided search of ERIC, ScienceDirect, and Google Scholar databases. The analysis highlights the flipped classroom’s effectiveness in enhancing EFL learners’ speaking, listening, and reading skills, fostering autonomy, and reducing language learning anxiety. Despite its benefits, challenges remain, including technological disparities, variations in student readiness, and insufficient teacher training. The review underscores the need for tailored professional development, equitable access to digital resources, and adaptive strategies to address these obstacles. Additionally, cultural and contextual factors significantly influence the model’s success, necessitating further exploration to optimize implementation in diverse educational settings. This systematic review contributes to the growing body of research on flipped classrooms, providing insights for educators and policymakers to enhance the efficacy of this approach. By addressing identified challenges and leveraging their advantages, the flipped classroom model offers a promising avenue for transforming EFL education and meeting the demands of 21st-century learners in a globalized world.
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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".