Bimtek Pengembangan Soal Berstandar TIMSS/ PISA Bagi Guru IPA Se- Kabupaten Tanggamus
Bibliographic record
Abstract
Kegiatan bimtek ini bertujuan untuk: (1) memberikan keterampilan kepada guruguru IPA di Kabupaten Tanggamus dalam mengembangkan bank soal kemapuan literasi berstandar TIMS/PISA; (2) memberikan keterampilan kepada guru-guru IPA di Kabupaten Tanggamus dalam mengembangkan bank soal kemapuan numerasi berstandar TIMS/PISA; (3) memberikan keterampilan kepada guru-guru IPA di Kabupaten Tanggamus dalam mengembangkan bank soal kemapuan sains berstandar TIMS/PISA. Peserta kegiatan bimtek ini adalah guru IPA di Kabupaten Tanggamus. Peserta pelatihan, panitia, dan narasumber melakukan kegiatan dengan menerapkan protocol pencegahan covid-19 yaitu menjaga jarak aman 1,5-2 meter dan menggunakan masker/faceshield. Evaluasi keberhasilan pelatihan akan dilakukan pada: (1) Awal kegiatan, yaitu pretes, untuk mengetahui sejauh mana kemampuan peserta bimtek tentang asesmen, PISA/TIMSS, literasi sains dan numerasi, serta analisis butir soal; (2) akhir kegiatan yaitu postes, berisikan pertanyaan yang sama dengan tes awal, untuk mengetahui tingkat keberhasilan, sehingga dapat diterapkan oleh setiap peserta. Berdasarkan hasil pretes dan postes diketahui terdapat peningkatan pemahaman guru dengan kategori sedang (Ngain = 0,42). Berdasarkan hasil feedback yang diberikan peserta diharapkan dilaksanakan kegiatan lanjutan dari kegiatan pelatihan ini.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.100 | 0.027 |
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".