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Record W4384932022 · doi:10.5539/ies.v16n4p21

Think-Pair-Share in the Development of Grade 7 Student Multiply and Divide Decimal Mathematical Achievement

2023· article· en· W4384932022 on OpenAlexvenueno aff
Pragaifa Peanark, Maporn Khamsim, Apantee Poonputta

Bibliographic record

VenueInternational Education Studies · 2023
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsnot available
FundersMahasarakham University
KeywordsRubricMathematics educationDecimalTest (biology)Achievement testPsychologyAcademic achievementComputer scienceMathematicsArithmeticStandardized test

Abstract

fetched live from OpenAlex

In this study, the effectiveness of a learning management plan using the think-pair-share technique on students' learning of decimal multiplication and division was investigated. The quasi-experimental study involved 42 grade 7 students in a public school in Thailand, who were simple random sampling. The study used a learning management plan, a learning achievement test, and a scoring rubric as instruments and employed the effectiveness index and a paired samples t-test to analyze the results. The results showed that the think-pair-share technique was effective in improving the student’s understanding of decimal multiplication and division and that the students demonstrated learning outcomes on most topics and met the requirements on the post-test. The study also found that there was a significant improvement in the student’s scores on the pre-test and post-test.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.287
GPT teacher head0.512
Teacher spread0.225 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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