Abstract
Abstract
Background
Adaptive training is an approach in which training variables change with the needs and traits of individual trainees. It has potential to mitigate the effect of personality traits such as impulsiveness on surgical performance. Selective performance feedback is one way to implement adaptive training. This paper investigates whether selective feedback can direct performance of trainees of either high- or low impulsiveness.
Methods
A total of 83 inexperienced medical students of known impulsiveness performed a four-session laparoscopic training course on a Virtual Reality Simulator. They performed two identical series of tasks every session. During one series of tasks they received performance feedback on duration and during the other series they received feedback on damage. Performance parameters (duration and damage) were compared between the two series of tasks to assess whether selective performance feedback can be used to steer emphasis in performance. To assess the effectiveness of selective feedback for people of high- or low impulsiveness, the difference in performance between the two series for both duration and damage was also assessed.
Results
Participants were faster when given performance feedback for speed for all exercises in all sessions (average z-value = − 4.14, all p values < .05). Also, they performed better on damage control when given performance feedback for damage in all tasks and during all sessions except for one (average z-value = − 4.19, all but one p value < .05). Impulsiveness did not impact the effectiveness of selective feedback.
Conclusion
Selective feedback on either duration or damage can be used to improve performance for the variable that the trainee receives feedback on. Trainee impulsiveness did not modulate this effect. Selective feedback can be used to steer training focus in adaptive training systems and can mitigate the negative effects of impulsiveness on damage control.
Publisher
Springer Science and Business Media LLC
Subject
Education,General Medicine
Cited by
2 articles.
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1. Novel applications of deep learning in surgical training;Artificial Intelligence, Big Data, Blockchain and 5G for the Digital Transformation of the Healthcare Industry;2024
2. Multi-modal classification of cognitive load in a VR-based training system;2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR);2023-10-16