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Record W2895046245

Using Pictorial Maps to Scaffold Problem Solving in Primary-Grade Arithmetic

2018· article· en· W2895046245 on OpenAlexaff
Massalin Sriutai, Surapon Boonlue, Jariya Neanchaleay, Elizabeth Murphy

Bibliographic record

VenueInternational Journal of Innovation in Science and Mathematics Education · 2018
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBlankWord (group theory)Representation (politics)Computer sciencePoint (geometry)ArithmeticWord problem (mathematics education)Mathematics educationControl (management)MathematicsArtificial intelligenceGeometry
DOInot available

Abstract

fetched live from OpenAlex

In this study, primary-grade students learned to solve and create arithmetic word problems using a three-phase process of visual representation. The study compared an experimental group (n=32) of third graders in Thailand using pictorial maps with a control group (n=31) using text-based problems. The visual representations called pictorial maps are unique in that they focus on place (location) in order to situate math problems in authentic contexts. In phase 1, students were given a pictorial map with imprinted objects representing keywords to help them solve a word problem. In phase 2, they used a blank pictorial map on which they could place plastic chips with imprinted images representing the problem’s keywords. In phase 3, they used a blank sheet with cut-outs of images representing keywords that they could use to represent their own word problems. Results revealed significantly higher post-test scores for the experimental group. Implications point to the value of mathematics’ teachers working with art teachers in their school to identify ways to use drawing to support representations of keywords and of other elements in word problems.

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)0.003
metaresearch head score (Gemma)0.004
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.052
GPT teacher head0.375
Teacher spread0.324 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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Same venueInternational Journal of Innovation in Science and Mathematics EducationSame topicCognitive and developmental aspects of mathematical skillsFrench-language works237,207