Video Scribe Media Development Management In Improving Arabic Speaking Skills
Bibliographic record
Abstract
One of the relevant learning media today to improve Arabic speaking skills is audio-visual media in Video scribe. The development of video scribe media is very urgent to do. Because the characteristics of video scribe-based learning can help students to understand and improve Arabic speaking skills by presenting images, sounds, animations, and designed learning materials interestingly to achieve the expected learning objectives, this research, and development (RD) at Kiai Haji Achmad Siddiq State Islamic University (UIN KHAS) Jember has provided a solution to the lack of Arabic learning media. Based on the test results of the validator (media, material, and design experts) on the development of the video scribe media, the score from the validator was based on the value component, namely the score from the learning media expert was 90% with a very valid/decent category, from the Arabic learning material expert got a score of 92 % with very valid/decent category. Moreover, the score from the design expert is 92% in the very valid/decent category. The field trial results using students’ response questionnaire instrument denoted that video scribe media for Arabic speaking skills learning, in general, achieved 47.1%, which indicates that video scribe is an exciting media to use in learning Arabic speaking skills. Besides, the lecturer responded during implementing video scribe media that it was a suitable medium for the pandemic. Thus, it became a solution in learning Arabic speaking skills. Based on these data, the video scribe media developed is feasible to learn Arabic speaking skills.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".