Optimalisasi Keterampilan Berbicara melalui Penerapan Metode Silent Way Berbantuan Google Talk
Bibliographic record
Abstract
Penelitian ini bertujuan untuk meningkatkan keterampilan berbicara siswa melalui metode pembelajaran silent way berbantuan google talk. Penelitian tindakan kelas ini diterapkan pada siswa kelas XI-IPA1 SMAN 1 Pati semester ganjil tahun ajaran 2017/2018 dengan dua siklus. Pengumpulan data menggunakan lembar observasi dan rubrik penilaian speaking. Analisis data dilakukan dengan menghitung frekuensi pengulangan berbicara dengan google talk dan persentase siswa dalam setiap level mulai dari terendah ke tertinggi (very poor sampai excellent). Hasil penelitian menunjukkan bahwa penerapan metode ini mampu meningkatkan keterampilan berbicara dengan indikator keberhasilan yakni penurunan frekuensi pengulangan berbicara dan peningkatan persentase siswa pada level excellent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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".