The Impact of the Flipped Classroom Teaching Model on EFL Learners’ Language Learning: Positive Changes in Learning Attitudes, Perceptions and Performance
Bibliographic record
Abstract
Instruction in English as a foreign language (EFL) learning is a priority around the globe, but instructional methodologies have not always kept pace with the changing needs of learners. The traditional teacher-centered EFL classroom teaching model can no longer meet the needs of college EFL learners to strengthen and improve their language ability. For years, the flipped classroom teaching model has been widely recognized as an innovative and effective instructional method by language educators. Based upon the analysis of the current EFL teaching and learning situation and the flipped classroom teaching model, the author took two Artificial Intelligent classes from a Chinese public college as the participants in the experiment to explore the impact of the flipped classroom teaching model on their language learning. One Artificial Intelligent class, the Experimental Group (EG), adopted the flipped classroom teaching model in EFL class, and the other Artificial Intelligent class, the Control Group (CG), adopted the traditional teacher-centered method in EFL class. After the survey, implementation of different teaching models, pre-test and post-test comparison, learning time changing curve analysis, and analysis of learners’ acceptance of the new model, the study aims to find out the impact of the flipped classroom teaching model on college EFL learners’ language learning attitudes, perceptions and performance, providing some references for college EFL educators on their EFL teaching to a certain extent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".