ANALISIS ASSESMEN LITERASI JASMANI DENGAN KEBUTUHAN PEMBELAJARAN PJOK DI SEKOLAH DASAR MUHAMMADIYAH TASIKMALAYA
Bibliographic record
Abstract
Fokus kajian dalam artikel ini ialah mengenai assesmen literasi jasmani. Pengukuran tingkat literasi jasmani yang terdiri dari empat domain tes yaitu : Daily Behaviour (prilaku sehari-hari), Physical Competence (kompetensi jasmani), Motivation and Confidence (motivasi dan kepercayaan diri) dan Knowledge and understanding (Pengetahuan dan Pemahaman). Tujuan analisis ialah untuk memberikan gambaran dan rumusan-rumusan masalah baru mengenai bagaimana konsep literasi jasmani, bagaimana cara mengukurnya dan bagaimana peluang instrument ini dapat diterapkan sebagai alternatif alat tes dan assesmen pada pembelajaran Pendidikan jasmani dan olahraga. Metode yang digunakan ialah studi pustaka berupa sebelas jurnal Internasional bereputasi, dua jurnal nasional terakdreditasi sinta, satu seminar nasional, satu dari Lembaga international physical literacy dan satu dari asosiasi guru Pendidikan jasmani Indonesia. Seluruh sumber tersebut berkaitan dengan literasi jasmani dari mulai konsep dan pengembangan instrumen. Analisis tiap butir tes literasi jasmani dibandingkan dengan instrument-instrumen tes kepenjasan yang sering dilakukan di SD Muhammadiyah Tasikmalaya. Dalam hal ini Tes kebugaran jasmani Indonesia (TKJI) yang sering dilakukan oleh guru Penjas disandingkan dengan tes literasi jasmani yaitu Canadian assessment Physical literacy (CAPL). Hasilnya bahwa CAPL lebih mencakup seluruh aspek hasil belajar yaitu afektif, kognitif dan psikomotor sedangkan TKJI yang sering dilakukan hanya mencakup psikomotor. Peluang digunakanaya alat tes literasi jasmani perlu beberapa kajian beberapa hal diantaranya : kebutuhan pengguna terhadap penilaian penjas yang komprehensif, kesinambungan pembelajaran penjas setiap jenjang, dan data literasi jasmani siswa yang valid.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".