Bibliographic record
Abstract
The importance of classroom interaction in language learning is self-evident. Among the various kinds of interactions, teacher questioning and student answering is an important aspect. This paper, on the basis of background knowledge of classroom questioning and the empirical study, first discuss the results of the experiment and the reasons, then put forward some useful techniques and principles in classroom questioning. At last the author got the conclusion: Effective use of questioning is a critical asset in every good teacher's toolbox. A good teacher uses questions at the right level and follows good questioning techniques. Key words: questioning, technique, EFL class Resume: L’importance de l’interaction de classe dans l’apprentissage de la langue est evidente. Parmi les diverses sortes d’interations, l’enseignant questionnant et l’apprenant repondant est un aspect important. Cet essai, sur la base des connaissances contextuelles de la question en classe et l’etude empirique, discute d’abord les resultats de l’experimentation et les raisons, puis propose des techniques et principes utiles de la question en classe. Enfin, l’auteur tire la conclusion : l’utilisation effective de la question est un capital critique pour chaque bon enseignant. Un bon professeur utilise les questions au niveau approprie et suit de bonnes techniques de la question. Mots-Cles: question, technique, classe ALE(Anglais Langue Etrangere)
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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".