PENINGKATAN HASIL BELAJAR DAN KETERAMPILAN PROSES SAINS SISWA MELALUI PENGGUNAAN LKPD BERBASIS INKUIRI PADA MATERI STRUKTUR DAN FUNGSI JARINGAN TUMBUHAN
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui peningkatan hasil belajar dan keterampilan proses sains siswa melalui penggunaan LKPD berbasis inkuiri pada materi struktur dan fungsi jaringan tumbuhan di MAN 2 Model Medan. Jenis penelitian ini ada Penelitian Tindakan Kelas (PTK). Desain penelitian menggunakan model Kemmis dan MC.Taggart yang terdiri dari empat komponen yaitu: “perencanaan, implementasi/pelaksanaan tindakan, observasi/pengamatan dan refleksi. Subjek penelitian adalah siswa kelas XI IPA 2 MAN 2 Model Medan Tahun Pembelajaran 2018/2019. Objek penelitian ini berupa hasil belajar siswa dalam ranah kognitif dan keterampilan proses sains pada materi Struktur dan Fungsi Jaringan Tumbuhan dengan menerapkan LKPD berbasis inkuiri. Hasil penelitian ini adalah penggunaan LKPD berbasis inkuiri dapat meningkatkan hasil belajar Biologi siswa di kelas XI IPA2 MAN 2 Model Medan T.P 2018/2019. Siswa yang sudah mencapai KKM pada siklus I 38,10% siswa dengan nilai rata-rata 75,5. Siswa yang sudah mencapai KKM pada siklus II 85,71% , dengan nilai rata-rata 93,8. Penggunaan LKPD berbasis inkuri dapat meningkatkan keterampilan proses sains siswa (mengamati, bertanya, memprediksi, mengkomunikasi dan menyimpulkan) pada kegiatan pembelajaran Biologi di kelas XI IPA2 MAN 2 Model Medan. Kata kunci: Hasil belajar, keterampilan proses sains, LKPD, inkuiri
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 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".