MENINGKATKAN PROFESIONALISME GURU DALAM MELAKSANAKAN PEMBELAJARAN FISIKA MELALUI BIMBINGAN TEKNIK KERJA KELOMPOK
Bibliographic record
Abstract
The objective in this research is to improve the professionalism ofteachers in implementing the guidance of physics learning through groupwork techniques in Sub Rayon 05 SMA Negeri 1 Percut Sei Tuan. Thesubjects were high school physics teachers are included in Sub Rayon 05 SMA Negeri 1 Percut Sei Tuan, amounting to 15 people. The research methodapplied is Action Research School through two cycles, with each cycleconsisting of planning, implementation, observation and reflection. Results ofdata analysis showed that (1) through the application of technical guidance tothe group work of teachers in the Physics Sub Rayon 05 SMA Negeri 1 Percut Sei Tuan, their knowledge of the methods, strategies, models and learningapproach that includes the model of physics learning experience improvement.(2) There is an increase in the ability of physics teachers in Sub Rayon 05SMA Negeri 1 Percut Sei Tuan in implementing models learning toimplement technical guidance group work. (3) Through the implementation oftechnical assistance work group, an increase in the ability of physics teachersin Sub Rayon 05 SMA Negeri 1 Percut Sei Tuan in preparing lesson plan inaccordance with the model or learning strategies. (4) There is an increase inthe ability of physics teachers in Sub Rayon 05 SMA Negeri 1 Percut SeiTuan in making the assessment instrument.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".