Research on Online Classroom Language between Teachers and Students
Bibliographic record
Abstract
Under the new situation, online live teaching has become one of the important teaching methods. The verbal interaction between teachers and students is an important explicit behavior in online live teaching. To further improve the quality of teacher-student verbal interaction in an online live classroom, this study constructs a speech interaction behavior coding system for teachers and students in an online live classroom, which is based on the ITIAS teacher-student interaction coding system and Bellack’s interaction structure theory. By observing the online live class of Luoyang No.56 middle school, conclusions are found in terms of interactive structure, the level of interaction, the interactive atmosphere, and the interactive dynamics. Online Classroom Language Between Teachers and Students infulences the effectiveness of the classes greatly. According to the study, the researcher put forward some strategies to improve the effectiveness of teacher-student verbal interaction in online live classrooms. The researcher believed that this research will give important guidance for the development of the school-based online live curriculum, and promote the development of the school-based online live curriculum.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".