PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF TERINTEGRASI ETNOSAINS UNTUK MENINGKATKAN KEMAMPUAN LITERASI SAINS DAN MOTIVASI BELAJAR PESERTA DIDIK PADA MATERI HIDROLISIS GARAM
Bibliographic record
Abstract
Pengembangan media pembelajaran interaktif terintegrasi etnosains perlu dilakukan karena media jenis ini belum tersedia, terutama pada materi hidrolisis garam. Penelitian bertujuan untuk mendeskripsikan kelayakan (validitas, kepraktisan, dan keefektifan) media pembelajaran interaktif menggunakan terintegrasi etnosains untuk meningkatkan kemampuan literasi sains dan motivasi belajar peserta didik. Penelitian ini menggunakan desain model penelitian pengembangan 4D (Define, Design, Developmet, Disseminate). Uji coba media pembelajaran dilakukan di Kelas XI IPA SMA Negeri 3 Martapura tahun ajaran 2021/2022. Hasil penelitian menunjukkan bahwa pengembangan media pembelajaran berdasarkan pada kriteria: (1) Validitas; ditinjau dari aspek isi, penyajian, bahasa, dan media memperoleh skor rata-rata 95,32 (sangat valid). (2) Kepraktisan, ditinjau dari hasil keterbacaan media pembelajaran pada uji perseorangan sebesar 4,08 (baik) dan uji kelompok kecil sebesar 4,32 (sangat baik), Hasil angket repon peserta didik adalah 3,9 (baik), dan hasil observasi keterlaksanaan pembelajaran adalah 4,51 (sangat baik), (3) Keefektifan, ditinjau dari peningkatan kemampuan literasi sains dengan skor N-gain 0,49 (sedang) dan motivasi belajar dengan skor N-gain 0,61 (sedang). Dengan demikian, media pembelajaran interaktif terintegrasi etnosains pada materi hidrolisis garam layak digunakan untuk meningkatkan kemampuan literasi sains dan motivasi belajar peserta didik pada pembelajaran kimia.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".