What can ‘professional vision’ tell us about teachers’ language alternation? A multimodal study of Chinese L2 classrooms
Bibliographic record
Abstract
Second-language (L2) teachers routinely switch between the target language and a shared lingua franca to secure students’ understanding and participation, yet differences between novice and expert language alternation remain under-described. Drawing on six hours of video-recorded Chinese L2 classroom interaction, this study compares 151 language alternation episodes produced by novice teachers with 40 episodes by expert teachers. Using Multimodal Conversation Analysis, the results show that both groups use language alternation proactively and retroactively. Novices alternate more often and less precisely, sometimes replacing emerging target forms; experts switch sparingly and embed English within a richer multimodal repertoire to maximize learning opportunities. Findings show that ‘professional vision’ guides teachers’ multimodal language alternation, and the resulting interactional design makes that vision visible. This study provides actionable insights for teacher educators seeking to help novices ‘learn to see’ and calibrate their use of shared 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".