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Record W4312132683 · doi:10.54097/hbem.v4i.3506

The ZPD Perspective on Teachers' Question-Answer Strategies In Mathematics Classroom

2022· article· en· W4312132683 on OpenAlexaff
Yujie Jiang, Yihan Wu, Xiyun Sha, Yuchun Xu

Bibliographic record

VenueHighlights in Business Economics and Management · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsZone of proximal developmentPerspective (graphical)Class (philosophy)Mathematics educationConversationSelection (genetic algorithm)Process (computing)Computer sciencePsychologyPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.233
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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