Construction of Chinese Language Teaching and Training Platform for International Students Based on Multimodal Tourism Language Landscape
Bibliographic record
Abstract
Current Chinese language teaching resources are often limited to traditional forms such as textbooks, PPTs, videos, etc., lacking diverse teaching methods. There is a significant gap between the language environment in real life and the content of classroom teaching, which cannot fully meet the learning needs of international students from different countries and language backgrounds. Therefore, this article constructed a Chinese language teaching and training platform for international students based on a multimodal tourism language landscape. This article first provided an overview of the definition and characteristics of multimodal concepts and tourism language landscapes, and elaborated on the current situation and problems of Chinese language teaching for international students. Subsequently, the Chinese language teaching and analysis for international students were introduced, and the application of multimodal tourism language landscape in Chinese language teaching was listed. Then, a cloud environment based on multimedia network was constructed for platform operation, and a virtual simulation training platform and different module functions were designed under situational teaching mode. Finally, the Chinese language teaching and training platform for international students can be put into practical application. The experimental results demonstrated that the average exam scores of students in the listening, speaking, reading, and writing modules were 28.56, 26.14, 24.56, and 27.34, respectively, all showing excellent progress. The impact of multimodal teaching on the learning of Chinese dietary vocabulary is particularly significant. The performance gap between groups began to show, especially after 22.5 hours, when the total scores of the three groups of students significantly widened, with nearly half of the participants preferring multimodal teaching methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".