The Impact of the Flipped Classroom Teaching Model on EFL Learners’ Language Learning: Positive Changes in Learning Attitudes, Perceptions and Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it