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Record W3171457687 · doi:10.29303/jcar.v1i2.781

Upaya Meningkatkan Hasil Belajar Peserta Didik Pada Materi Agribisnis Pengolahan Hasil Nabati Kelas XII SMKN 1 Sakra

2019· article· id· W3171457687 on OpenAlexaff
Rohatin Arpianingsih

Bibliographic record

VenueJournal of Classroom Action Research · 2019
Typearticle
Languageid
FieldSocial Sciences
TopicSTEM Education
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

Untuk mengetahui peningkatan hasil belajar siswa kelas XI SMKN 1 Sakra pada materi pengolahan hasil nabati dengan menggunakan model pembelajaran berbasis masalah. Penelitian ini merupakan penelitian tindakan kelas yang dilaksanakan di SMKN 1 Sakra yang terdiri dari dua siklus dimana tiap siklus meliputi 4 tahap yaitu perencanaan, pelaksanaan tindakan, pengamatan dan refleksi. Data yang digunakan adalah hasil belajar siswa, hasil observasi guru dan hasil observasi siswa. Sabjek penelitian ini adalah ini adalah siswa kelas XII SMKN 1 Sakra yang berjumlah 30 orang. Hasil penelitian ini menunjukan bahwa model pembelajaran berbasis masalah mampu mendorong pemikiran siswa untuk berkembang dnegan cepat dimana aktivitas yang mendukung proses belajar mengajar terus mengalami peningkatan dan aktivitas yang tidak relevan dengan kegiatan proses belajar mengajar berkurang dari pertemuan kepertemuan berikutnya. Berdasarkan hal ini maka dapat disimpulkan bahwa penerapan odel pembelajaran berbasis masalah dapat meningkatkan hasil belajar peserta didik pada materi pengolahan hasil nabati

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)0.002
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 score0.031
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0310.008

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 head0.125
GPT teacher head0.440
Teacher spread0.315 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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