PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS VIDEO SPARKOL DENGAN MENGGUNAKAN MEDIA INTERNET DALAM PEMBELAJARAN MATEMATIKA SMP NEGERI 1 DAN 2 KECAMATAN TANAH JAWA
Bibliographic record
Abstract
The existence of the internet and all existing facilities can provide new knowledge or browse teaching materials for teachers so that the internet is an easier learning medium and can enrich teachers' insights. This study aims to determine the use of sparkol video as a learning resource for mathematics teachers of SMP Negeri Tanah Jawa District. Sparkol video-based internet learning media is made with the Videoscribe application, as a learning medium that can be used in schools and outside of school. The research aims to develop learning media that can be accessed freely on the Internet. Sparkol video-based internet learning model used is videoscribe which is open and mass. The learning curriculum also provides a set of learning tools covering, various questions, quizzes, and forms of assignments. The research implementation steps follow the general pattern of scientific research with an object-oriented approach with the stages of identification (identification), analysis (analysis), design (design), coding (programming) and testing (testing). This study produced an outcome that was targeted at the results of this study in the form of articles to be published in an Accredited National Journal. This article also aims to disseminate the results of research and science that can be used to broaden the knowledge and development of science in the future, especially in finding association rules between indicators of achieving competency standards tested in mathematics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 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".