Pursuing student response through incomplete syntax, prosody, bodily- and visuo-orthographical resources in Chinese-as-a-second-language classrooms
Bibliographic record
Abstract
This study investigates how teachers use designedly incomplete utterances (DIUs) and other multimodal resources to pursue student responses to their questions to accomplish particular pedagogical tasks in Chinese as a second language (CSL) classrooms. Adopting interactional linguistics and conversation analysis, we examined 18.5 hours of CSL classroom interactions. We identified two different types of DIUs based on their syntactic projectability: DIU with a local projection and DIU with a global projection. Both types of DIUs are used after a lack of student answers to teachers’ prior questions. However, DIUs with a local projection are used after teachers’ identification or characterisation questions, whereas DIUs with a global projection are used to pursue student answers to teachers’ telling questions. These two types of DIUs are produced with the prosodic feature of final lengthening, bodily movements such as torso lean and eyebrow raise, and visuo-orthographical resources such as Chinese characters on blackboards and screens. The findings contribute to our understanding of the multimodal resources that teachers use to pursue responses in L2 classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".