PENYUSUNAN RENCANA PELAKSANAAN PEMBELAJARAN (RPP) KURIKULUM 2013 MELALUI PENDAMPINGAN UNTUK MENINGKATKAN KOMPETENSI GURU UPT SMP NEGERI 42 KECAMATAN MEDAN DELI TAHUN PELAJARAN 2018/2019
Bibliographic record
Abstract
Preparation of the 2013 Curriculum Learning Implementation Plan (RPP) through Assistance to Improve the Competence of Upt SMP Negeri 42 Medan Deli District Teachers for the 2018/2019 Academic Year. The purpose of this school action research (PTS) is to increase the level of understanding of teachers, especially at UPT SMP Negeri 42 Medan in preparing and developing the 2013 Curriculum Learning Implementation Plan (RPP) through mentoring. The method used in this research is descriptive method. This research is in the form of School Action Research (PTS). The subjects of this research were 12 UPT SMP Negeri 42 Medan teachers from various subjects. The data collection technique used is in the form of observation sheets, interviews, discussions, documentation studies. Based on the results of observations of the first cycle of improvement actions using the 2013 curriculum Learning Implementation Plan (RPP) assessment format, the value reached 39.6 which means it is in the sufficient category, and the results of observations using the teacher activity assessment format in the Learning Implementation Plan Preparation Process (RPP) during the Assistance in Compiling the 2013 Curriculum RPP the score reached 36, which means it is in the good category. 2) Through the 2013 Curriculum Learning Implementation Plan (RPP) assessment format in cycle II which achieved a score of 86.7 was in the very good category.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".