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Record W4410063940 · doi:10.61116/jiim.v2i2.470

PEMANFAATAN SIMULASI DIGITAL UNTUK MEMFASILITASI PEMBELAJARAN KONSEP ABSTRAK DALAM FISIKA

2024· article· id· W4410063940 on OpenAlexaff
LAILA MAGFIRAH

Bibliographic record

VenueJurnal Ilmiah IPA dan Matematika (JIIM) · 2024
Typearticle
Languageid
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.005

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.

Opus teacher head0.009
GPT teacher head0.243
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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