A 3D Multi-User Virtual Laboratory: Is Successful Implementation Enough?
Bibliographic record
Abstract
Is successful implementation enough? Using Hiltz & Johnson’s (1989) definition of successful implementation, as adoption or acceptance, we discuss the evaluation of a 3D multi-user virtual laboratory for the teaching of radiopharmacy. The views and experiences of educators and learners in Canada and Europe are gathered. Multiple users can enter the virtual laboratory, move an avatar around and communicate with each other while interacting with the virtual world and associated interactive learning resources. Qualitative and quantitative data gathered from three studies suggests that learning effectiveness, satisfaction and acceptance of the virtual laboratory are extremely high in Canada where most learners experienced a sense of presence and became quickly immersed, and where instructor acceptance is extremely high, however, European educators revealed concerns over adoption. We conclude that successful implementation requires not only good participatory design and successful implementation, but also support for educators in integrating it into their teaching practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".