Brunei Teachers’ Perspectives on Questioning: Investigating the Opportunities to “Talk” in Mathematics Lessons
Bibliographic record
Abstract
A teachers’ practice cannot be characterised by a single lesson, hence comparison is best made with lesson sequences that better sample the diversity of a teacher’s practice. In this study, we video recorded lesson sequences in four Year 8 mathematics classrooms, as well as interviewed each of the four teachers in Brunei Darussalam. Because of our methodology and based on the findings from the richness in the data that was collected, there were some features in the video and interview data that emerged. One of the features is the significant short utterances made by the students as well as their respective teachers, and the extent of the teachers’ own and their students’ questioning behaviours in the lessons as perceived by the teachers themselves during the video-stimulated recall interviews. In the four Brunei classrooms that we studied, most of the lessons were so rushed, the teachers did most of the talking and when teachers and students do interact, it almost always involved faster-paced exchanges between them. Thus, restricting students to single words (“yes” or “no”) or short choral responses. Overall, the findings appear to indicate that short utterances implied that there were less (or even no) opportunities for fuller student participation in classroom discussions.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".