Evaluation of research related to virtual physics laboratory applications
Bibliographic record
Abstract
In studies conducted in the last 30 years, different types of virtual labs were applied to different study groups using very different methods and techniques. The aim of this study is to evaluate the virtual labs used in teaching physics in terms of the purpose of use, working groups, methods, and techniques using the content analysis method based on a literature review conducted. The results of the application, which is aimed at the selection and use of virtual physics lab programs that offer very different designs, scopes, and means of application and at the adaptation of such programs to the target audience by educators, as well as at provision of a means for measurement and evaluation, will be analyzed. By examining the data thus obtained in terms of the subject matter, method, and outcome, it will be possible to assist the parties who will utilize the virtual physics laboratory programs in teaching physics to use such programs with expected efficiency.
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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 | 0.173 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".