Bibliographic record
Abstract
In English teaching classrooms, the English language is not only the language for teaching and communication but the teaching contents and objectives. In addition, teacher talk is also the important source of learners’ English input. This is especially true in the case of China where teacher-oriented teaching still dominates the classroom; besides, students can hardly gain an access to other forms of comprehensible in put outside of the classroom since English is a foreign language. This essay tries to analyze college English teachers’ talk from the perspective of Baktin’s dialogue theory. It aims to open out the use of TT in the present classrooms and meanwhile explore the ways to improve the quality of it. The research is conducted in Henan Polytechnic University, and two research methods are adopted: case study and survey study. In the case study, six college English teachers’ classes were recorded, and the recordings were transcribed. In the survey study, the author designed one questionnaire, in which more than 150 students took part in the research. Through close analysis of the transcription, the essay elaborated such questions as feature and amount of TT, teacher’s questioning, teacher’s feedback, etc. According to the results of the analysis, the author put forward suggestions to improve the quality of TT and students’ talking skills.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".