Exploring the Long-Term Effect of the Flipped Learning Model in Primary English Classrooms
Bibliographic record
Abstract
The flipped learning model has proven more effective in enhancing each aspect of language learning. However, a study is necessary to examine the long-term effects of the flipped learning model within primary educational contexts. To address this research gap, the present study examines the effectiveness of the flipped learning model in terms of long-term learning outcomes in English language teaching within primary education. In the study, 56 students participated. The participants were divided into two groups: flipped and non-flipped classrooms. The flipped classroom received treatment through the flipped learning model, and the non-flipped classroom received treatment through the conventional learning model. The data were collected four times using pre-test, post-test, mid-test, and delayed post-test. The study demonstrated that the flipped learning model was more effective than the conventional learning model in enhancing the retention of subject-specific knowledge over an extended period. Specifically, the findings showed that students improved their performance from the pre-assessment through the following assessments, including mid-tests and post-tests. The study highlights the effectiveness of the flipped learning model relative to conventional practices in fostering long-term knowledge retention, suggesting that its implementation could improve educational outcomes within primary programs.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".