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Problem-solving in virtual environment simulations prior to direct instruction for differential diagnosis in medical education: An experimental study.

Christian FässlerTanmay SinhaChristian Marc SchmiedJörg GoldhahnManu Kapur
Published in: MedEdPublish (2016) (2023)
Background: Despite acquiring vast content knowledge about the functioning of the human body through university teaching, medical students struggle to transfer that knowledge to one of the core disciplinary practices - differential diagnosis. The authors aimed to overcome this problem by implementing computer-based virtual environment simulations in medical education courses. Methods: In an experimental study, the authors compared problem-solving in medical computer-based virtual environment simulations prior to instruction with an instruction-first approach. They compared the effects on isomorphic testing and transfer performance of clinical knowledge and clinical reasoning skills as well as evoked learning mechanisms. The study took place in spring 2021 with undergraduate medical students in the scope of a medical trajectory course. Due to Corona-Virus-19 measures participants completed all study activities remotely from home. Results: The authors did not find any learning activity sequence to be superior to the other. However, when looking at the two learning activities individually, they found that problem-solving in computer-based virtual environment simulations and direct instruction might be equally effective for learning content knowledge. Nevertheless, problem-solving in computer-based virtual environment simulations with formative feedback might be more effective for learning clinical reasoning skills than mere instruction. Conclusions: The findings indicate that problem-solving in computer-based virtual environment simulations might be more effective for learning clinical reasoning skills than mere theoretical instruction. The present study has a high level of ecological validity because it took place in a realistic setting where students had to perform all learning and testing tasks autonomously.
Keyphrases
  • medical students
  • medical education
  • healthcare
  • molecular dynamics
  • deep learning
  • monte carlo
  • endothelial cells
  • machine learning
  • risk assessment
  • working memory
  • quality improvement
  • pluripotent stem cells