2021
DOI: 10.1017/s0022215121003352
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Automated summative feedback improves performance and retention in simulation training of mastoidectomy: a randomised controlled trial

Abstract: Objective This study aimed to investigate the effects of automated metrics-based summative feedback on performance, retention and cognitive load in distributed virtual reality simulation training of mastoidectomy. Method Twenty-four medical students were randomised in two groups and performed 15 mastoidectomies on a distributed virtual reality simulator as practice. The intervention group received additional summative metrics-based feedback; the control group followed standard instructio… Show more

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Cited by 8 publications
(6 citation statements)
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“…47 Indeed, automated summative feedback has been shown to improve performance and retention. 48 When utilising virtual reality prior to cadaveric dissection, residents displayed an improvement in performance. 49 This could be related to the lower cognitive load identified in virtual reality training compared to cadaveric training.…”
Section: Results and Analysismentioning
confidence: 99%
“…47 Indeed, automated summative feedback has been shown to improve performance and retention. 48 When utilising virtual reality prior to cadaveric dissection, residents displayed an improvement in performance. 49 This could be related to the lower cognitive load identified in virtual reality training compared to cadaveric training.…”
Section: Results and Analysismentioning
confidence: 99%
“…The use of summative feedback at the end of each session seems to motivate the learner and improve self-assessment. 20 Such integrated learning supports are a feature unique to VR simulation as this cannot easily be integrated in training on physical models. Further, VR simulation might lend itself to gamification strategies adopted from leisure PC gaming, which may prove further useful in future training routines for directed self-regulated learning in temporal bone surgery.…”
Section: Discussionmentioning
confidence: 99%
“…Sample size calculations are not well defined for repeated measures (i.e., multiple raters and procedures) (18). Based on the central limit theorem application described by Bloch and Norman (18), as well as extensive experience from previous similarly designed studies (19,20), we found that 12 participants in each group would be sufficient to detect a performance difference of 10% between groups (11).…”
Section: Sample Sizementioning
confidence: 94%