PENINGKATAN AKTIVITAS DAN HASIL BELAJAR FISIKA MATERI HUKUM TERMODINAMIKA MENGGUNAKAN APLIKASI ZOOM CLOUD MEETINGS PADA SISWA KELAS XI IPA-1 SMA NEGERI 1 SIBORONGBORONG SEMESTER 2 TAHUN PELAJARAN 2020/2021
Bibliographic record
Abstract
The research aims to improve the activity and learning outcomes of physics subject matter for the law of thermodynamics for class XI IPA-1 SMA Negeri 1 Siborongborong Semester 2 of the 2020/2021 academic year. The research was carried out in two cycles and each cycle consisted of four stages, namely planning, action, observation, and reflection. The research subjects were 36 students. Data collection techniques using the method of observation, documentation and tests. Data validity through triangulation. Data analysis with qualitative descriptive technique. The results showed that the activity of students in the initial study was 36.11% or 13 students became 72.22% in the first cycle or 26 students, and in the second cycle it became 94.44% or 34 students were declared complete. The increase in the average value in the initial study from 63.33 to 73.33 in the first cycle, and in the second cycle to 84.17 and an increase in learning completeness from 10 students or 27.78% to 21 students or 58.33% and 32 students or 88.89% in the second cycle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".