Comparing 3D Virtual Labs and Traditional Labs: Impact on Teacher Training and Student Learning in Physics Education
Bibliographic record
Abstract
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.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".