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Impact of compulsory participation of medical students in a multiuser online game to learn radiological anatomy and radiological signs within the virtual world Second Life.

Teodoro Rudolphi-SoleroRocío Lorenzo-ÁlvarezMiguel José Ruiz-GómezFrancisco Sendra-Portero
Published in: Anatomical sciences education (2021)
Competitive game-based learning within Second Life enables effective teaching of basic radiological anatomy and radiological signs to medical students, with good acceptance and results when students participate voluntarily, but unknown in a compulsory context. The objectives of this study were to reproduce a competitive online game based on self-guided presentations and multiple-choice tests in a mandatory format, to evaluate its development and student perceptions compared to a voluntary edition in 2015 (N = 90). In 2016 and 2017, respectively, 191 and 182 third-year medical students participated in the game as a mandatory course activity. The mean (±SD) score of the game was 74.7% (±19.5%) in 2015, 71.2% (±21.5%) in 2016, and 67.5% (±21.5%) in 2017 (P < 0.01). Participants valued positively the organization and educational contents but found the virtual world less attractive and the game less interesting than in the voluntary edition. The experience globally was rated with 8.2 (±1.5), 7.8 (±1.5), and 7.1 (±1.7) mean points (±SD) in a ten-point scale, in the 2015, 2016, and 2017 editions, respectively (P < 0.05). Competitive learning games within virtual worlds like Second Life have great learning potential in radiology, but the mean score in the game decreased, acceptance of virtual world technology was lower, and opinion about the game was worse with a compulsory participation, and even worse when dropouts were not allowed. Under the conditions in which this study was conducted, learning games in three-dimensional virtual environments should be voluntary to maintain adequate motivation and engagement of medical students.
Keyphrases
  • medical students
  • virtual reality
  • social media
  • physical activity
  • machine learning
  • artificial intelligence