Pendampingan Guru Biologi dalam Penyusunan Instrumen Penilaian Berorientasi HOTS di Kabupaten Lombok Barat
Bibliographic record
Abstract
Telah dilakukan kegiatan pendampingan pada guru Biologi dalam penyusunan instrumen penilaian berorientasi HOTS di Kabupaten Lombok Barat. Kegiatan pendampingan guru dalam penyusunan instrumen penilaian berorientasi HOTS ini dilaksanakan dalam bentuk (1) penyampaian materi secara klasikal, (2) pemberian contoh dalam membuat soal HOTS, dan (3) pendampingan guru dalam menyusun instrumen penilaian HOTS. Sebelum penyampaian materi dan latihan diberikan, terlebih dahulu dilakukan Survei dengan menggunakan angket. Hasil survei menunjukkan bahwa sebagian besar (55%) guru biologi yang ada di Kabupaten Lombok Barat belum pernah mengikuti kegiatan pelatihan pembuatan instrumen penilaian yang berorientasi HOTS. Semua guru (100%) setuju dan bersedia diberikan pelatihan terkait dengan HOTS. Kegiatan pelatihan dilaksanakan secara daring menggunakan zoom meeting. Peserta pelatihan terdiri dari 39 orang guru. Sebagian besar guru yang ikut pelatihan (52%) berasal dari Pulau Lombok. Sementara itu sisanya, 28% guru berasal dari Pulau Bali dan 20% guru berasal dari Pulau Sumbawa. Hasil pelatihan dan pendampingan menunjukkan bahwa guru-guru biologi yang ada di Kabupaten Lombok Barat masih mengalami kesulitan dalam pembuatan stimulus. Faktor penyebab guru mengalami kesulitan diantaranya disebabkan oleh kemampuan literasi dasar sekitar 30 – 40% dan kesulitan dalam melakukan kegiatan pengembangan media sekitar 50%. Karena itu, kegiatan pendampingan lanjutan yang melibatkan forum MGMP masih diperlukan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".