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Record W2273651816 · doi:10.14288/1.0101049

The effect of instruction in modular arithmetic on the ability of grade 6 students to divide fractions and give a rational explanation of the process

2011· article· en· W2273651816 on OpenAlexaff
Alexander David MacDonald

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArithmeticMathematics educationRational numberProcess (computing)MathematicsModular designComputer sciencePsychologyProgramming languageDiscrete mathematics

Abstract

fetched live from OpenAlex

The problem under investigation in this study was to find out what relationship a unit in modular arithmetic might have to Grade 6 pupils' skill in computing the division of fractions and to their understanding of the mathematical basis of the algorithm. It was hypothesized that a unit in modular arithmetic would aid in developing skill in computing and understanding of the algorithm. The study was conducted with a sample of 58 Grade 6 students from the same school. The subjects were assigned to two treatment groups. Both groups received a review of fraction concepts at the beginning of the study. Following this, one group was taught modular arithmetic while the other group reviewed adding and subtracting of fractions. Then both groups were taught multiplication and division of fractions. Following the instruction period, both groups were tested for ability to compute division of fractions. To test understanding of the division of fractions algorithm, an interview inventory test was administered to all subjects in both groups. A statistical analysis of the data from these tests revealed no support for the hypotheses. The conclusion was that teaching modular arithmetic to the Grade 6 pupils participating in the study did not appear to improve their ability to compute division of fractions nor their understanding of the mathematical basis of the division of fractions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.991

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 studies0.0000.000
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 head0.030
GPT teacher head0.269
Teacher spread0.239 · 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 designObservational
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

Citations0
Published2011
Admission routes1
Has abstractyes

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