A Study on the Synergy of Different Modes in MOOC for Teaching Chinese as a Foreign Language
Bibliographic record
Abstract
Guided by the theoretical framework of dynamic multimodal discourse analysis, this study examines the synergy among different modes in the multimodal discourse of MOOC for TCFL (Teaching Chinese as a Foreign Language). For this purpose, this study has been carried out using qualitative research methods to analyze the video of HSK Standard Course Level 1 --- Lesson 1 in Confucius Institutes Online from the perspective of the context of culture, the context of situation and the synergy between various modes. The results of this study show that on the one hand, in order to achieve different communicative goals, teachers will choose different modes of multimodal discourse communication. On the other hand, in the whole process of communication, auditory mode is the main mode, which is always in the foreground. The visual mode formed by teachers will be in the foreground only when they complement the auditory mode and form the complementary relationship. However, In the process of presentation, auditory mode and the visual mode formed by background images are both in the foreground and play a significant role in helping students understand in the teaching.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".