UPAYA MENGATASI MISKONSEPSI HIDROLISIS GARAM MENGGUNAKAN PENDEKATAN CONCEPTUAL CHANGE DENGAN MODEL FLIPPED CLASSROOM
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengatasi miskonsepsi pada materi hidrolisis garam dengan pendekatan conceptual change dengan model flipped classroom. Penelitian dilaksanakan di kelas XI MIPA 3 SMA Kristen PENABUR Kota Wisata, Cibubur, dengan jumlah siswa sebanyak 33 siswa. Penelitian ini dilakukan melalui metode penelitian tindakan kelas model Kemmis dan Mc Taggart yang terdiri dari 4 komponen, yaitu: perencanaan, tindakan, pengamatan, dan refleksi. Penelitian dilaksanakan dengan tiga siklus. Data diperoleh dari hasil wawancara, observasi kelas, refleksi siswa, pertanyaan terbuka dan instrumen tes two tier dan esai yang telah divalidasi. Analisis data dilakukan melalui analisis data kualitatif melalui proses reduksi data, koding dan kategori untuk menggambarkan perubahan miskonsepsi siswa. Hasil penelitian menunjukkan bahwa siswa mengalami miskonsepsi pada konsep mengenai garam, sifat asam basa larutan garam dan menghitung pH larutan garam. Pendekatan conceptual change dengan model flipped classroom telah memfasilitasi siswa untuk mengubah miskonsepsi yang dialami melalui beberapa proses tahap konfrontasi terhadap konsep awal siswa. Dengan demikian Pendekatan conceptual change dengan model flipped classroom dapat digunakan untuk mengatasi miskonsepsi pada pembelajaran kimia khususnya materi hidrolisis garam.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.013 |
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".