PENGEMBANGAN VIDEO PERMAINAN TEMATIK TEMA DIRIKU SUBTEMA AKU MERAWAT TUBUHKU
Bibliographic record
Abstract
Penelitian ini adalah penelitian pengembangan video menggunakan model ADDIE yang mencakup lima langkah, yaitu: (1) analisis (analyze), (2) perancangan (design), (3) pengembangan (development), (4) implementasi (implementation), dan (5) evaluasi (evaluation). Subjek uji coba dalam penelitian ini adalah peserta didik kelas I Sekolah Dasar di Kecamatan Buleleng pada tahun ajaran 2022/2023 dimana SD di Kecamatan buleleng terdiri dari 19 sekolah yang terbagi dalam 9 gugus.. Uji coba produk terdiri atas (1) Desain Uji Coba, (2) Subjek Uji Coba, (3) Jenis data (4) Tahap Review Para Ahli, (5) Instrument Pengumpulan Data, (6) Teknik Analisis Data. Data dikumpulkan melalui teknik observasi, wawancara, dan pemberian angket (kuestioner). Penelitian ini menunjukkan bahwa video permainan pendidikan jasmani olahraga dan kesehatan berbasis tematik tema diriku (Subtema Aku Merawat Tubuhku) untuk peserta didik kelas I Sekolah Dasar layak digunakan sebagai media pembelajaran di Sekolah Dasar dengan persentase ahli desain pembelajaran (75%), ahli media pembelajaran (100%), dan praktisi lapangan (94,4%0. Jadi dapat disimpulkan bahwa video permainan pendidikan jasmani olahraga dan kesehatan berbasis tematik tema diriku layak digunakan sebagai salah satu media pembelajaran. Kata Kunci : Video, Video Pembelajaran Tematik,Permainan Tematik.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".