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Record W4398785717 · doi:10.58540/sambarapkm.v2i2.548

Pemanfaatan Media Barang Bekas dalam Meningkatkan Daya Kreativitas Guru TK di Kecamatan Martapura Kabupaten Banjar

2024· article· id· W4398785717 on OpenAlexaff
Noor Baiti, Muhammad Noor, Alifah

Bibliographic record

VenueSAMBARA Jurnal Pengabdian Kepada Masyarakat · 2024
Typearticle
Languageid
FieldSocial Sciences
TopicEducation and Character Development
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysicsArtEngineering

Abstract

fetched live from OpenAlex

Pemanfaatan bahan bekas adalah karena pendidik kurang bisa berkreasi dalam proses pembelajaran, dimana media yang sering digunakan bersifat barang jadi atau yang sudah ada. Selain itu, dalam pelaksanaan pembelajaran, guru masih kurang optimal dalam menyediakan bahan yang diperlukan untuk meningkatkan kreativitas anak karena terbatas oleh pengetahuan, padahal benda-benda yang digunakan dalam kegiatan meningkatkan kreativitas anak bisa berupa bahan yang murah (atau barang bekas) atau tanpa mengeluarkan biaya. Salah satu cara untuk mengatasi permasalahan tersebut adalah diperlukannya media pembelajaran yang efektif. Salah satu media pembelajaran yang dapat digunakan adalah media barang bekas. Salah satu fungsi utama media pembelajaran adalah sebagai alat bantu mengajar yang turut mempengaruhi iklim, kondisi, dan lingkungan belajar yang ditata dan diciptakan oleh guru.Kegiatan pengabdian kepada masyarakat dilaksanakan dengan menggunakan cara tatap muka, metode ceramah dan demonstrasi serta pemahaman/pelatihan berupa pendampingan dalam praktek. Pelatihan ini bertujuan untuk pemanfaatan media bahan bekas untuk meningkatkan daya kreativitas guru. Hasil kegiatan pelatihan ini diharapkan dapat membantu guru dalam membuat media dari bahan bekas sehingga dapat meninkatkan daya kreativitas anak dalam belajar di kelas TK Kecamatan Martapura Kabupaten Banjar.

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)0.001
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: none
Teacher disagreement score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0380.007

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 head0.027
GPT teacher head0.294
Teacher spread0.267 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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