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Record W7116782656 · doi:10.15294/jpii.v14i4.21115

Comparing 3D Virtual Labs and Traditional Labs: Impact on Teacher Training and Student Learning in Physics Education

2025· article· W7116782656 on OpenAlexaff
Choojit Sarapak, Jutapol Jumpatam, Thodsaphon Lunnoo, Nipasak Kongngarm, Sayam Raso, K. Kearns

Bibliographic record

VenueJurnal Pendidikan IPA Indonesia · 2025
Typearticle
Language
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsResource (disambiguation)Significant differenceVirtual labConceptual changeTraining (meteorology)Conceptual frameworkPhysics educationVirtual learning environmentTeaching method

Abstract

fetched live from OpenAlex

Experiments are fundamental to physics education but are often hindered by resource constraints. This study investigates the effectiveness of teacher training using 3D Virtual Labs (3DVL) compared to Traditional Labs (TL) in teaching the electron charge-to-mass ratio (e/m). The research aimed to quantitatively compare the impact of these methods on teachers' and students' conceptual understanding, critical thinking, and experimental skills. A quasi-experimental design was employed involving 32 teachers and 131 students, utilizing a pre-test/post-test comparison structure. Teachers received training in either 3DVL or TL methods before applying the instruction in their classrooms. The results indicate that both 3DVL and TL groups improved significantly (p < .001) in conceptual mastery and problem-solving abilities. While no statistically significant difference was observed between the groups’ overall post-test scores, TL showed a slight advantage in problem-solving, whereas 3DVL was associated with higher student confidence and perceived experimental skill improvement. These findings suggest that 3DVL is a viable, cost-effective alternative to traditional equipment. The study concludes that integrating virtual simulations can effectively overcome infrastructure limitations and enhance learning outcomes in resource-constrained settings.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.399
Teacher spread0.313 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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
Published2025
Admission routes1
Has abstractyes

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