PENGARUH MODEL PEMBELAJARAN \nFLIPPED CLASSROOM MENGGUNAKAN MEDIA VIDEO TERHADAP HASIL BELAJAR IPAS SISWA KELAS IV \nSDN KAJAR TENGGULI
Bibliographic record
Abstract
Learning outcomes are an assessment of students' abilities as a measure to determine learning outcomes. In fact, based on observations made by researchers, students' science and science learning outcomes are still relatively low because students are still not actively involved in learning, so a learning model is needed that can make students actively involved (student centered) so that students' science and science learning outcomes are better. The aim of the research was to determine differences in students' science learning outcomes on energy changes material taught using the Flipped Classroom learning model in class IV of SDN Kajar Tengguli. \nThe research method used in this research is Pre Experimental Design with One Group Pretest-Posttest, namely using one class IV as the experimental class. Sampling was taken using a saturated sampling technique. Data collection is used by conducting tests. \nFrom the results of the Paired Samples t-Test research, a Sig value of 0.000 < 0.05 was obtained, which means that H0 was rejected and H1 was accepted. So, it is stated that there is a significant influence of the use of the Flipped Classroom learning model using video media on the science learning outcomes of class IV students. With an average pretest score (before being given treatment) of 67 and posttest (after being given treatment) of 84, there was an increase in the percentage of science learning results for class IV students by 26%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.020 | 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".