A Case Study of Visual-verbal Relations and Application Principles in China’s College English Classroom
Bibliographic record
Abstract
This study seeks to explore relations of visual-verbal modes and figure out application principles in China’s College English Classroom (CEC). It takes data from two files: (1) videos of two excellent CEC teachers; and (2) semi-structured interviews with them, within which it studies four modes in PPT or on blackboard presentation: image, words, dynamic and symbol. Three instruments—Multimodality annotation software ELAN, two-dimensional meaning-making tables and semi-structured interview are employed to facilitate both quantitative and qualitative analyses. The results showed features of frequency, timing and proportion of each mode summarized by ELAN and found out proper collocation of image, words, dynamic and symbol relies on intersemiotic relations, which are revealed as complementary and non-complementary. It further analyzed application principles of four modes under CEC context in China. Except principles of effectiveness, efficiency and appropriate collocation put forward by former studies, it complemented principle of modes’ transference to highlight the necessity to form students’ autonomy of visual-verbal modes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".