Automated summative feedback improves performance and retention in simulation training of mastoidectomy: a randomised controlled trial

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 instructions. Two to three months after training, participants performed a retention test without learning supports.

RESULTS: The intervention group had a better final-product score (mean difference = 1.0 points; p = 0.001) and metrics-based score (mean difference = 12.7; p < 0.001). At retention, the metrics-based score for the intervention group remained superior (mean difference = 6.9 per cent; p = 0.02). Also at the retention, cognitive load was higher in the intervention group (mean difference = 10.0 per cent; p < 0.001).

CONCLUSION: Summative metrics-based feedback improved performance and lead to a safer and faster performance compared with standard instructions and seems a valuable educational tool in the early acquisition of temporal bone skills.