PEMANFAATAN SIMULASI DIGITAL UNTUK MEMFASILITASI PEMBELAJARAN KONSEP ABSTRAK DALAM FISIKA
Bibliographic record
Abstract
Penelitian kajian literatur ini bertujuan untuk mengeksplorasi potensi pemanfaatan simulasi digital dalam memfasilitasi pembelajaran konsep-konsep abstrak dalam fisika. Kesulitan dalam memvisualisasikan dan memahami ide-ide fisika yang abstrak seringkali menjadi tantangan signifikan bagi peserta didik. Simulasi digital menawarkan representasi visual dan interaktif yang dinamis, yang berpotensi menjembatani kesenjangan antara konsep abstrak dan pemahaman konkret. Kajian ini menganalisis berbagai penelitian yang relevan terkait penggunaan simulasi dalam pengajaran fisika, mengidentifikasi jenis-jenis simulasi yang efektif, fitur-fitur desain yang mendukung pemahaman konseptual, serta dampak penggunaannya terhadap hasil belajar dan motivasi siswa. Lebih lanjut, penelitian ini juga membahas tantangan dan peluang implementasi simulasi digital dalam konteks pembelajaran fisika, serta memberikan rekomendasi untuk penelitian dan praktik di masa depan. Diharapkan, kajian literatur ini dapat memberikan wawasan yang komprehensif mengenai peran simulasi digital sebagai alat bantu yang efektif dalam membelajarkan konsep abstrak fisika.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 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".