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The Improvement reasoning ability of students to mathematic and logic through colour chip worksheet in the basic education unit in Tanjung Morawa

2019· article· en· W3009093010 on OpenAlexaff
Putri Khairiah Nasution, Aghni Syahmarani, Maulida Yanti

Bibliographic record

VenueABDIMAS TALENTA Jurnal Pengabdian Kepada Masyarakat · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWorksheetMathematics educationClass (philosophy)CurriculumProcess (computing)Test (biology)CreativityComputer sciencePsychologyMultimediaPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

Learning media is tools for teaching and learning process. Related to the use of instructional media as a component that can help teachers and students in the learning process. In learning mathematics at SDIT Deli Insani, there is still lack of learning media as a component in the learning process. The selection of the most appropriate media that must be considered to achieve the expected goals is to stimulate the ability to choose, increase communication power, stimulate independence, encourage motivation, and increase student creativity. The worksheet is a piece of learning media as well as an innovative game that can be carried around, the exam material is in accordance with the K-13 curriculum for elementary school students grade 1 to 4. By taking a sample of students grade 3 and 4 consist of 30 students per class, done a pre-test about material of mathematics, then explained the use of color chip worksheets. Next, learned the material using a worksheet of color chips. And finally, a post test was conducted to measure up which students could absorb the material. The evaluation, carried out about 2 months after the training, the teachers and students have independently used a worksheet of color chips and the students' abilities significantly improved.

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)0.003
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 score0.116
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.022
GPT teacher head0.334
Teacher spread0.312 · 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
Published2019
Admission routes1
Has abstractyes

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