MENINGKATKAN KOMPETENSI PEDAGOGIK UNTUK MENYUSUN RPP MELALUI BIMBINGAN SUPERVISI DI SMP NEGERI 2 MUKO-MUKO BATHIN VII
Bibliographic record
Abstract
The ability to plan lessons is an academic competency that teachers need to develop professionally. This study aims to improve the pedagogical competence of teachers at SMP Negeri 2 Muko-Muko Bathin VII in preparing lesson plans through supervision activities. This study used a School Action Research (PTS) design. This study was carried out in two cycles including PTS principles, namely planning, implementing, observing, and reflecting in each cycle. with descriptive qualitative data processing. The research subjects were teachers of SMP Negeri 2 Muko-Muko Bathin VII. Data collection techniques through observation, interviews, and document analysis. The results showed an increase in the ability of teachers to prepare lesson plans through supervisory guidance. The increase in ability can be seen from the results of the evaluation by applying supervisory guidance to teachers in cycle I and cycle II. The teacher shows seriousness and understands the preparation of RPP Curriculum 2013 after receiving guidance for the preparation of RPP. The results of the actions of each cycle show the results of cycle 1 with an average value of 63.75, and cycle 2 with an average value of 77.50. There is an increase in the total value from 50% to 100%. Thus it can be emphasized that supervision guidance can be used as an alternative to improve the pedagogical ability of SMP Negeri 2 Muko-Muko teachers in preparing lesson plans.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".