English Instructors’ Use of Classroom Questions and Question Types in EFL Classrooms
Bibliographic record
Abstract
The aim of this study was to identify the degree to which English teachers apply classroom questioning skills from the point of view of English teachers in the Saudi context. I used a descriptive approach in this study and video recordings and questionnaires to collect data. The questionnaire consisted of three axes (skills for formulating classroom questions, skills for asking classroom questions, and skills dealing with students’ answers). The sample was 160 English teachers from intermediate government schools. Video cameras recorded English as a foreign language (EFL) teachers’ explanations and all the questions they asked in their classrooms. The study findings showed that most EFL teachers apply the various skills related to classroom questions. First, they use skills for dealing with student’s answers. Second, they use the skill of formulating classroom questions. Finally, they practice asking classroom questions. The findings further revealed that the majority of Saudi English teachers tend to use closed, lower-order, and display questions often in their classrooms. By contrast, the results showed that they seldom used higher-order, open-ended, and referential questions in EFL classrooms.
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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 | 0.009 | 0.049 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".