PENERAPAN PEMBELAJARAN INKUIRI MODEL ALBERTA UNTUK MENINGKATKAN HASIL BELAJAR SISWA SMP PADA MATA PELAJARAN TIK
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui hasil belajar siswa setelah penerapan pembelajaran inkuiri Model Alberta. Metode Penelitian yang digunakan adalah metode kuasi eksperimen. Penelitian dilakukan pada siswa kelas VIII SMP Negeri 1 Margaasih Tahun Ajaran 2010-2011 pada standar kompetensi menggunakan perangkat lunak pengolah angka untuk menyajikan informasi. Objek penelitian terdiri atas dua kelas, yaitu kelas eksperimen yang menggunakan pembelajaran inkuiri model Alberta dengan jumlah sampel sebanyak 38 siswa dan kelas kontrol yang menggunakan metode pembelajaran konvensional dengan jumlah siswa 38 orang. Hasil penelitian menunjukkan peningkatan hasil belajar siswa pada kelas dengan pembelajaran inkuiri Model Alberta dengan pencapaian nilai rata-rata 3,45 dengan nilai indeks gain sebesar 0,37. Sedangkan peningkatan hasil belajar pada kelas konvensional dengan pencapaian nilai rata-rata 2,24 dengan nilai indeks gain sebesar 0,24. Hasil ini menunjukkan adanya peningkatan hasil belajar dengan pembelajaran inkuiri Model Alberta dibandingkan dengan pembelajaran konvensional. Berdasarkan hasil tersebut penerapan pembelajaran inkuiri model Alberta berpengaruh positif terhadap hasil belajar siswa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".