PELATIHAN PENYUSUNAN INSTRUMEN ASESMEN PENILAIAN AKHIR BAGI GURU MATEMATIKA SEKOLAH MENENGAH ATAS DI KOTA SURABAYA
Bibliographic record
Abstract
Kualitas sistem pembelajaran di tingkat Sekolah Menengah Atas (SMA) ditentukan oleh beberapa aspek, antara lain aspek perencanaan, pelaksanaan, dan penilaian hasil belajar siswa. Capaian hasil belajar siswa dapat diukur dari proses pengumpulan dan pengolahan data dari suatu penilaian akhir, sehingga dibutuhkan suatu instrumen yang tepat guna mendukung penilaian akhir tersebut. Berkaitan dengan hal tersebut, Tim Pengabdian Kepada Masyarakat (PPM) Program Studi Pendidikan Matematika Universitas PGRI Adi Buana Surabaya menyelenggarakan pelatihan penyusunan instrumen asesmen penilaian akhir bagi guru SMA se-Kota Surabaya. Pelatihan tersebut mampu membantu para guru dalam menyusun instrumen penilaian akhir siswa. Respon peserta terhadap penyelenggaraan kegiatan pelatihan ini sangat baik, terlihat dari para guru mampu mengimplementasikan materi yang didapat dalam penyusunan instrumen di masing-masing mata pelajaran yang diampu. Hal tersebut tentunya sangat mendukung proses penilaian pembelajaran, sehingga indikator pembelajaran dapat tercapai dengan baik.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.015 |
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".