Credibility of a simulation-based virtual laboratory: An exploratory study of learner judgments of verisimilitude
Bibliographic record
Abstract
Several studies have examined realism and instructional effectiveness of physical simulations. However, very few have touched on the question of their credibility or verisimilitude, from the user’s point of view. This article presents an empirical exploratory study which investigated the perceptions of potential users of a simulation-based virtual physics laboratory (the VPLab). In the VPLab, students conduct virtual physics experiments designed to promote both acquisition of general experimental skills and conceptual learning. The objectives of the study were to uncover (1) users’ preoccupations and representations related to the VPLab’s verisimilitude, (2) the cues enabling users to make judgments of verisimilitude about the VPLab, and (3) the roles played by these cues in the expression of user judgments. Following a qualitative and descriptive approach, the study included in-depth interviews with thirteen first-year university science students. As part of the results, the complex and idiosyncratic nature of user verisimilitude judgments was highlighted. Furthermore, connections were established between these judgments and individual traits of users, such as prior use of certain computer applications. The influence of various aspects of the environment on its verisimilitude was also considered. These aspects included features expected to favor the VPLab’s credibility, such as video sequences of actual experiments.
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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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".