Pelatihan Penyusunan RPP IPA Blended Learning Berbasis TPACK Sebagai Upaya Guru dalam Menghadapi Pembelajaran Pasca Covid-19
Bibliographic record
Abstract
Pengabdian ini dilaksanakan dengan tujuan memberikan pelatihan bagi guru dalam menyusun Rencana Pelaksanaan Pembelajaran (RPP) blended Learning dan berbasis Technological Pedagogical Content Knowledge (TPACK) sehingga guru dapat melaksanakan pembelajaran sesuai dengan protokol kesehatan pascapandemi Covid-19. Pasca Covid-19 guru diharapkan dapat merancang dan melaksanakan pembelajaran blended learning. Sebagai penunjang terlaksananya blended learning, guru diberikan pelatihan penyusunan RPP berbasis TPACK. Uraian kegiatan pelatihan tersebut yaitu guru diberikan materi membuat RPP merdeka dan blended learning, serta evaluasi secara online dilanjutkan dengan materi RPP berbasis TPACK. Hari kedua, guru diberikan pelatihan bagaimana menyusun RPP Ilmu Pengetahuan Alam (IPA) blended learning dan berbasis TPACK sesuai dengan kondisi dan situasi sekolah, hari ketiga dilaksanakan evaluasi terhadap RPP yang sudah dibuat. Hasil yang dicapai adalah guru memahami materi yang disampaikan dengan baik dan dapat mengimplementasikan materi dan waktu penyampaian materi yang sudah baik sekali.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.033 |
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".