Pengembangan Media Pembelajaran Berbasis Multimedia pada Mata Kuliah Bakery
Bibliographic record
Abstract
Based on preliminary observations, the learning outcomes and creativity of IKK Department students who take the Bakery course are still low, it is estimated that the low learning outcomes are due to the limitations of existing learning resources or learning media, there are still deficiencies in the existing learning media. Therefore, designed and made a learning medium in the form of multimedia learning for Bakery courses. The purpose of this research was to develop the learning media based multimedia that valid, practice, and effective for bakery. This research used Research and Development (R&D) method, and 4D (four-D) model that develop by S. Thiagarajan and friends. There were four steps in 4D model, there were define, design, develop, dessiminate. The media was diesigned by using Macromedia Flash CS 6. The result of this research was that the learning media based multimedia were valid, practice, and effective to use as a learning media of bakery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; both teacher heads agree on what is shown here.
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".