Gamified Learning in a Virtual World for Undergraduate Emergency Radiology Education: Quasi-Experimental Study
Bibliographic record
Abstract
Background: Emergency radiology is essential for future doctors, who will face urgent cases requiring radiologic diagnosis. Using virtual simulations, gamified clinical scenarios, and case-based learning enhances practical understanding, develops technical and communication skills, and fosters educational innovation. Objective: This study aimed to assess the feasibility of learning emergency radiology in the virtual world Second Life (Linden Lab) through a gamified experience by evaluating team performance in clinical case resolution, individual performance on seminar assessments, and students' perceptions of the activity. Methods: Teams of 3-4 final-year medical students, during a 2-week radiology clerkship, had access to 7 clinical cases in virtual clinical stations and were randomly assigned 2 to solve and submit. They later discussed the cases in a synchronous virtual meeting and attended an emergency radiology seminar. The experience was repeated over 2 consecutive years to assess reproducibility through comparison of learning outcomes and students' perceptions. Learning outcomes were evaluated through team-based case resolution and individual seminar assessments. Students' perceptions were gathered via a voluntary questionnaire including 5-point Likert scale items, cognitive load ratings, 10-point evaluations, and open-ended comments. Results: In total, 182 students participated in 2020-2021 and 170 in 2021-2022, demonstrating strong team-based case resolution skills with mean scores of 7.36 (SD 1.35) and 8.41 (SD 0.99), respectively (P<.001). The perception questionnaire had a 90.6% response rate. The highest cognitive load was observed in avatar editing (median 7, 95% CI 6.56-6.96). Case-solving cognitive load was significantly lower in 2021-2022 compared with 2020-2021 (median 6, 95% CI 5.69-6.21 vs 5.10-5.66; P<.001). The students rated the experience highly, with average scores exceeding 8.0 out of 10 across various aspects. Notably, the highest-rated aspects were the teaching staff (9.13, SD 1.15), cases (8.60, SD 1.31), project organization (8.42, SD 1.67), and virtual rooms (8.36, SD 1.62). The lowest-rated aspect was internet connectivity (6.68, SD 2.53). Despite the positive scores, all aspects were rated significantly lower in 2021-2022 compared with 2020-2021. These year-to-year comparisons in performance and perception support the reproducibility of the experience. Conclusions: This study demonstrates that a game-based learning experience in the Second Life virtual world, combining virtual clinical scenarios and team-based tasks, is feasible and reproducible within a radiology clerkship. Students showed strong performance in case resolution and rated the experience highly, within a playful context that integrated asynchronous and synchronous activities. Lower ratings in the second year may reflect contextual differences, such as changes in COVID-19 pandemic restrictions.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".