Using the Virtual World of Second Life in Veterinary Medicine: Student and Faculty Perceptions
Bibliographic record
Abstract
Virtual worlds are emerging technologies that can enhance student learning by encouraging active participation through simulation in immersive environments. At Ross University School of Veterinary Medicine (RUSVM), the virtual world of Second Life was piloted as an educational platform for first-semester students to practice clinical reasoning in a simulated veterinary clinical setting. Under the supervision of one facilitator, four groups of nine students met three times to process a clinical case using Second Life. In addition, three groups of four clinical faculty observed one Second Life meeting. Questionnaires using a 4-point Likert scale (1=strongly disagree to 4=strongly agree) and open-ended questions were used to assess student and clinical faculty perceptions of the Second Life platform. Perception scores of students (M=2.7, SD=0.7) and clinical faculty (M=2.7, SD=0.5) indicate that Second Life provides authentic and realistic learning experiences. In fact, students (M=3.4, SD=0.6) and clinical faculty (M=2.9, SD=1.0) indicate that Second Life should be offered to future students. Moreover, content analyses of open-ended responses from students and faculty support the use of Second Life based on reported advantages indicating that Second Life offers a novel and effective instructional method. Ultimately, results indicate that students and clinical faculty had positive educational experiences using Second Life, suggesting the need for further investigation into its application within the curriculum.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".