Practice of Online and Offline Blended Teaching Curriculum in Korean Chinese Education Based on Flipped Classroom
Bibliographic record
Abstract
Since the 21st century, with the deepening of education reform, "flipped classroom" as a new teaching method has attracted high attention from countries around the world. Especially in foreign language teaching, flipped classrooms, with their flexible learning methods, greatly enhance learners' learning enthusiasm and enhance learning efficiency. On the basis of analyzing the current situation of Chinese language teaching in South Korea, this article conducted a more in-depth study on the application status of "online and offline" blended flipped classrooms in China. This article provided a detailed analysis from two aspects: teaching design and teaching implementation and student feedback. Combined with practical teaching practices, it was proved that "flipped classroom" can better promote the development of students' Chinese language. This article focused on case studies, supplemented by empirical analysis. The language skills test scores of the experimental group students were higher than those of the control group. For example, class 3 of the experimental group achieved a high score, while the highest score in the control group was only 71 points. This article has certain reference significance for Chinese language teaching in South Korea, and has certain reference significance for teaching other languages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".