Media Pembelajaran Teknologi Audio-Visual Pada Model Teams Games Tournament (TGT) Untuk Meningkatkan Pengetahuan IPA SDK Kelas V Manumuti di Daerah Perbatasan Timor Leste
Bibliographic record
Abstract
Education is very important for the development of children's knowledge, so it needs to be supported by effective learning media. Learning media are familiar to a teacher such as textbooks, teaching aids and other media. However, after observations were made at one of the Class V Manumuti SDKs, in the Timor Leste Border area, it was found that teachers predominantly taught using textbooks and no other learning media. The existing conditions result in students learning monotonously, students being less active, teachers not being creative and students not understanding science lessons well. So in this research, learning media will be created that are fun and not monotonous in order to arouse students' motivation to study science. In this case, the media used is Audio-Visual Technology Learning Media. This media will be elaborated in the Teams Games Tournament (TGT) model, where students will be formed into several groups, then given a science knowledge game, and students compete with their opposing groups. That way, a sense of responsibility, healthy competition, imagination, and student activity in class dominate and students will be motivated to learn science because the end of this learning model is an award or reward for students who become winners. To measure the attainment of science knowledge, observation, discussion, tests, scores and audio-visual media are used. It is hoped that the results of the research will ensure that students in the Timor Leste border areas do not miss out on technological knowledge, teachers will be more creative and students will be able to play an active role and increase their motivation to learn science.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".