The ZPD Perspective on Teachers' Question-Answer Strategies In Mathematics Classroom
Bibliographic record
Abstract
As teaching methods continue to change, the ability of students to think and solve problems autonomously has become one of the main focuses of teaching, and therefore teachers' question answering strategies have become essential. Many researchers have raised the importance of the zone of proximal development theory in question answer strategies, but few have summarized the specific type of question-answer strategies need to be used in different scenarios. For this reason, this paper will research on teacher question-answer strategies in mathematics classrooms from the perspective of ZPD. A case study was conducted based on two videos of mathematics class, and qualitative analysis was conducted through conversation analysis transcript. Research shows that the selection of question-answer strategies is related to identifying the zone of proximal development of students. If students’ questions are in their ZPD, then teacher will allow learners to explore the solution on their own and will follow up by asking more questions to students. On the other hand, answers should be provided directly for removing meaningless barriers that restricts discovery. Having different question-answer strategies is to balance learners’ cost of learning in the discovery process. This will enhance students to be autonomous learners by reaching achievable learning goals at potential developmental levels.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".