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Record W2728060417 · doi:10.5539/ies.v10n7p48

The Stages of Student Mathematical Imagination in Solving Mathematical Problems

2017· article· en· W2728060417 on OpenAlexvenueno aff
Teguh Wibowo, Akbar Sutawidjaja, Abdur Rahman As’ari, I Made Sulandra

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsMathematical problemMathematical modelMathematics educationMathematical theoryMathematical structureImaginationMathematical logicPsychologyComputer scienceMathematicsCognitive psychologyAlgorithm

Abstract

fetched live from OpenAlex

This research is a qualitative study that aimed to describe the stages of students mathematical imagination in solving mathematical problems. There are three kinds of mathematical imagination in solving mathematical problems, namely sensory mathematical imagination, creative mathematical imagination and recreative mathematical imagination. Students can produce one kind of mathematical imagination or other kinds of mathematical imagination. Problem sheet is used as a supporting instrument to find out the stages of students mathematical imagination in solving problems. Three students are used as research subjects in whom students were able to produce their mathematical imagination in solving mathematical problems. The results showed that there are three stages of students mathematical imagination in solving mathematical problems, the first stage is sensory mathematical imagination, the second stage is creative mathematical imagination, and the last stage is recreative mathematical imagination.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.204
GPT teacher head0.525
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2017
Admission routes1
Has abstractyes

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