"Give me one reason why this is true": A multimodal investigation of the strategies used by university teachers of mathematics to elicit responses from students
Bibliographic record
Abstract
This study aims to broaden the current understanding of undergraduate university mathematics teaching by conducting a multimodal analysis of video-recorded segments of classroom teaching by an experienced professor and a less experienced Teaching Assistant (TA). The study focuses on the participants" use of discursive strategies and multimodal features including gestures, gaze, and facial expressions, as they are used to elicit responses from students and promote participation in the classroom. The analysis is conducted through the qualitative multimodal thematic coding of video-recorded segments, and audio transcripts, and with the quantitization of the participants" strategies used to elicit student responses. Findings show that despite variance in the teaching location, educational backgrounds, and levels of experience of the participants, the discursive and multimodal strategies that they used during teaching are remarkably similar; in other words, the participants use the same genre of teaching undergraduate mathematics. Findings also reveal subtle differences between the teaching practices of the experienced professor and less experienced TA. Conclusions present undergraduate mathematics teaching as a complex, multimodal, and wholly interactive genre of teaching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".