Model Pembelajaran Flipped Classroom untuk Peningkatan Hasil Belajar Matematika Siswa SMP
Bibliographic record
Abstract
Kemampuan yang didapat siswa setelah mempelajari matematika disebut hasil belajar matematika, salah satunya ialah kemampuan kognitif. Rendahnya kemampuan kognitif mempengaruhi hasil belajar siswa dan permasalahan tersebut harus diatasi guru. Tujuan penelitian ini untuk melihat variasi peningkatan hasil belajar matematika antara siswa yang diterapkan model flipped classroom dan yang tidak diterapkan model tersebut. Pendekatan yang dipakai yaitu Kuantitatif dengan jenis rancangan Quasi-experiment dan desain Non-equivalent (pre-test and post-test) control group. Populasi yaitu siswa kelas VIII dari sebuah SMP Negeri di Aceh, Indonesia. Sampel yang dipilih yaitu VIII-2 (kelas eksperimen) dan VIII-6 (kelas kontrol) secara simple random sampling. Instrumen penelitian melibatkan Instrumen utama berupa soal tes Teorema Pythagoras, sedangkan perangkat pembelajaran yang di gunakan adalah Rencana Pelaksanaan Pembelajaran (RPP), Lembar Kerja Siswa (LKS) dan video pembelajaran. Teknik pengumpulan data memanfaatkan dua tes (pre-test dan post-test). Teknik analisis data dilakukan uji-t pada taraf signifikansi 5% dari nilai N-Gain untuk mengamati adanya perbedaan peningkatan hasil belajar matematika siswa di dua kelas, sesudah prasyarat pengujian terpenuhi. Hasil penelitian: 1) Meningkatnya hasil belajar matematika siswa lebih baik saat diterapkan model flipped classroom daripada tidak diterapkan model tersebut; 2) Hasil belajar kelas eksperimen mendapat peningkatan di kategori tinggi sedangkan kelas kontrol di kategori sedang.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".