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Record W4385496352 · doi:10.31219/osf.io/w78jb

MENINGKATKAN HASIL BELAJAR PESERTA DIDIK MATA PELAJARAN ILMU PENGETAHUAN SOSIAL MENGGUNAKAN MODEL PEMBELAJARAN CONTEXTUAL TEACHING AND LEARNING (CTL) BERBANTU MEDIA MINIATUR LINGKUNGAN ALAM

2023· preprint· id· W4385496352 on OpenAlexaff
Nadya Pratiwi

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPsychologyMathematics educationPedagogyArt

Abstract

fetched live from OpenAlex

Penelitian ini mengkaji mengenai studi literatur. Metode studi literatur adalah serangkaian kegiatan yang berkenaan dengan metode pengumpulan data pustaka, membaca dan mencatat, serta mengelolah bahan penelitian. Berhasilnya tujuan pembelajaran ditentukan oleh banyak faktor diantaranya adalah faktor guru dalam melaksanakan proses belajar mengajar, karena guru secara langsung dapat mempengaruhi, membina dan meningkatkan kecerdasan serta keterampilan peserta didik. Penggunaan model Contextual Teaching and Learning (CTL) berbantu media Miniatur Lingkungan Alam dan Buatan memberikan pengalaman nyata, berfikir tingkat tinggi, berpusat pada peserta didik, kritis dan kreatif, pengetahuan bermakna dalam kehidupan, dekat dengan kehidupan nyata, adanya perubahan prilaku serta pengetahuan. Selain itu hasil belajar peserta didik dapat meningkat

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.006

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 head0.076
GPT teacher head0.330
Teacher spread0.253 · 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 designNot applicable
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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