Pengaruh Model Pembelajaran Discovery Learning terhadap Hasil Belajar Siswa Kelas X SMA Swasta Sinar Pancasila Betun
Bibliographic record
Abstract
This study aims to determine the effect of the application of the Discovery Learning learning model on student learning outcomes in Geography class X at Sinar Pancasila Betun Private High School. The study used an experimental method with a pretest-posttest control group design. The study population was all students of class X, with a purposive sampling technique consisting of an experimental class and a control class. The research instrument was a multiple-choice learning outcome test that had been tested for validity and reliability. The results showed a significant increase in learning outcomes in the experimental class after the application of the Discovery Learning learning model. The average posttest score of the experimental class was 74.0236, higher than the control class of 19.2045. Hypothesis testing using the t-test showed that the calculated t value > t table at a significance level of 5%, so the alternative hypothesis was accepted. This means that there is a positive effect of the use of the Discovery Learning learning model on student learning outcomes. Based on these findings, it is concluded that the Discovery Learning learning model is effective for improving student learning outcomes in Geography learning and can be used as an alternative innovative learning strategy in schools.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".