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Record W2095313575 · doi:10.5539/ies.v7n1p69

Proportional Reasoning: How do the 4th Graders Use Their Intuitive Understanding?

2013· article· en· W2095313575 on OpenAlexvenueno aff
Sylvana Novilia Sumarto, Frans van Galen, Zulkardi Zulkardi, Darmawijoyo Darmawijoyo

Bibliographic record

VenueInternational Education Studies · 2013
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
FundersUniversitas SriwijayaUniversiteit Utrecht
KeywordsProportional reasoningMemorizationMathematics educationPsychologyProportionality (law)Value (mathematics)MathematicsStatistics

Abstract

fetched live from OpenAlex

In Indonesia, the proportion is being taught formally in Grade 5 (10-11 years old). However, the existing learning approach does not support the development of the students’ proportional reasoning. The way to teach proportion by giving cross multiplication is not meaningful for the students. They just memorize the procedure without understanding how it works. Within a design research, a learning sequence was developed for Grade 4 students (9-10 years old) in order to develop their proportional reasoning as well as their ability to solve the proportional problem before they learned more formally in Grade 5. The students of Grade 4 might have an intuitive understanding about proportionality and they might be able to deal with the comparison problem. How do they use this intuitive understanding to solve the comparison problem and what kind of difficulties that they faced? These questions were addressed through the analyzing of the students’ work on the pretest and the video of students’ interview. The result shows that the students’ intuitive understanding, in principle, can help them to deal with the comparison problem. The students’ experience may lead them to use the concept of proportionality instead of the absolute value in simple comparison problem.

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)0.000
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 score0.217
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.182
GPT teacher head0.383
Teacher spread0.202 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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