Pengembangan Media Variasi Vising (Video Passing) Sepak Bola untuk Pembelajaran Kelas VIII Sekolah Menengah Pertama
Bibliographic record
Abstract
The development of communication and information technology has a great influence on the application of media as a means of learning activities, but these advances have not been used ideally in instructional learning in schools. This study aims to produce a variety of football visioning media products for class VIII Junior High School learning. This research is a Research and Development using the ADDIE (Analyze-Design-Development-Implement-Evaluate) learning design model. As subjects in this study, namely Physical Education Teachers and Students. The object of this research is the variation of soccer vision media for junior high school students. Data collection techniques using a questionnaire. The results of the research data in the form of quantitative data were analyzed descriptively quantitatively. The feasibility of video media was obtained through four stages with an average percentage: 1) material expert validation obtained 67.91%, 2) media expert validation obtained 94.37%, 3) corner teacher response obtained 88.15%, 4) student responses obtained 82.9%. The average total assessment of video media is 83.33% with a classification of "very good", so it can be concluded that the basic soccer technique video media for junior high school students is declared suitable for use by teachers in the process of delivering material.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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, 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".