  Virtual reality simulation is becoming popular as a training platform in
surgical education. However, one important aspect of simulation-based surgical
training that has not received much attention is the provision of automated
real-time performance feedback to support the learning process. Performance
feedback is actionable advice that improves novice behaviour. In simulation,
automated feedback is typically extracted from prediction models trained using
data mining techniques. Existing techniques suffer from either low
effectiveness or low efficiency resulting in their inability to be used in
real-time. In this paper, we propose a random forest based method that finds a
balance between effectiveness and efficiency. Experimental results in a
temporal bone surgery simulation show that the proposed method is able to
extract highly effective feedback at a high level of efficiency.
