Upaya Meningkatkan Hasil Belajar IPA Siswa dengan Menggunakan Metode Demonstrasi pada Kelas VI SD Negeri 01 Durian Tinggi Kecamatan Kapur IX Kabupaten Lima Puluh Kota
Bibliographic record
Abstract
This study aims to improve student learning outcomes in class VI State Elementary School 01 Durian Tinggi, because so far classical learning models have not been able to improve student learning outcomes. Data collection is carried out by observation, tests, and observation sheets. The results of the observations are then analyzed and made as an action planning material in the next cycle and then processed in order to draw conclusions. Based on preliminary observations, the initial data obtained about student learning outcomes is that 4 people get very good grades, 7 people get good grades, 20 people get pretty good grades and 1 person gets bad grades. Then the average grade obtained is 73.125. After being implemented in the first cycle, 10 people got very good grades, 15 people got good grades, 6 people got good grades and 1 person got bad grades. The average score of students in cycle I was 78.75. And after implementing the second cycle, 12 people get very good grades, 21 people get good grades, and no one gets good enough grades and less grades. The average score of students in cycle II is 83.98. Based on the results above we can conclude that student learning outcomes have experienced a very good improvement.Implications of the results of Classroom Action Research (CAR) by using the Demonstration method can improve the students’ learning outcomes and also have a positive effect on the activities of class VI students of SDN 01 Durian Tinggi.
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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 | 0.001 | 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) | 0.018 | 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; 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".