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Record W2945334759 · doi:10.30743/mes.v4i1.867

PENGARUH PENGGUNAAN MODEL CONCEPT ATTAINMENT TERHADAP PEMAHAMAN KONSEP MATEMATIKA

2018· article· en· W2945334759 on OpenAlex
Helma Mustika, Endang Sutriana

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMES Journal of Mathematics Education and Science · 2018
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsThe Audio Recording Academy
Fundersnot available
KeywordsMathematics educationNormalityTest (biology)Class (philosophy)Statistical hypothesis testingSample (material)PsychologyMathematicsComputer scienceStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract. The purpose of this research is to know the understanding of mathematical concepts of students with the use of conceptual attainment model is better than the understanding of students' mathematical concepts with conventional learning. This research is quasi experimental research. The research design used was randomized subjects posttest only control group design. By selecting a Class VIII-2 sample as an experimental class and class VIII-1 as a control class of analytical techniques using the t-test as a hypothesis test, the prerequisite test is a normality test and homogeneity test. Based on the hypothesis test, t-test, obtained the price tarithmetic = 3.073 and price ttable = -1.997 at the real level of 0.05. Because tarithmetic > ttable, so Ha accepted and H0 rejected. So it can be concluded that the ability to understand the concept of mathematics students using conceptual learning model attainment better than the ability to understand the concept of mathematics students using conventional learning model.Keywords: Concept Attainment, Understanding of Concept

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.

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)0.002
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 score0.166
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.083
GPT teacher head0.405
Teacher spread0.322 · 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