Bibliographic record
Abstract
English is a main subject in Chinese English classes, which requires plenty of practice, needs cooperation between the teacher and students in class to jointly fulfill the verbal communication and the teaching-learning procedure. Classroom questioning, the skill of the elicitation method of teaching that is student-oriented and advocated today, gives an incentive to communicative activities in English. Raising questions effectively is a major method of the teacher who guides his students to think actively, fostering students’ ability of analysis and creation. It is also an essential way for the teacher to output information and obtain feedback, and an important channel to exchange ideas between the teacher and students. Therefore, the teacher must pay great attention to the skill of asking questions in English class. Each question must be presented to accomplish the teaching objective and task. Only in this way may the English teachers ask question effectively and improve the skill of questioning so as to make contribution to our Chinese English education. To begin with some elemental definitions, this paper discusses some basic knowledge of questions, and then explores the skill of questioning in English class about the preparing, designing, controlling and evaluating of questioning. Finally the benefit of the skill is studied with abundant teaching cases. The paper analyzes tentative in English class by integrating theory with practice. Thus, the skills of questioning are further understood in English classes.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".