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Record W4323074830 · doi:10.18502/kss.v8i4.12929

The Effectiveness of The Utilization of Mathematical Pyramid Media on Academic Achievement of Students in Mathematics Education Study Program

2023· article· en· W4323074830 on OpenAlexaff
Sri Rahmah Dewi Saragih, Syahriani Sirait, Nida Yusriani, Rumondang, Yen Aryni

Bibliographic record

VenueKnE Social Sciences · 2023
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsMathematics educationMathematicsClass (philosophy)HomogeneousTest (biology)Test scoreStatisticsStandardized testCombinatoricsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This study aims to see the effectiveness of the use of mathematical pyramid media in learning media courses to improve the academic achievement of fourth semester students of the Mathematics Education Study Program, FKIP UNA, Academic Year 2019/2020. The sample of this research is two classes, namely class IVA (experimental class) and class IVB (control class). The pretest mean value of the experimental class (64.82) with the highest value of 82 and the lowest score of 46, the pretest value of the control class (61.48) with the highest value of 78 with the lowest score of 42. From the pretest homogeneity test there was no difference in variance or the two samples homogeneous where Fcount ttabel = 2.01 which means that Ha is accepted. Keywords: Mathematics pyramid media; academic achievement; learning media

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)0.001
metaresearch head score (Gemma)0.005
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.181
GPT teacher head0.511
Teacher spread0.330 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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