Understanding the effects of structured self-assessment in directed, self-regulated simulation-based training of mastoidectomy: A mixed methods study

Objective: Self-directed training represents a challenge in simulation-based training as low cognitive effort can occur when learners overrate their own level of performance. This study aims to explore the mechanisms underlying the positive effects of a structured self-assessment intervention during simulation-based training of mastoidectomy.

Methods: A prospective, educational cohort study of a novice training program consisting of directed, self-regulated learning with distributed practice (5x3 procedures) in a virtual reality temporal bone simulator. The intervention consisted of structured self-assessment after each procedure using a rating form supported by small videos. Semi-structured telephone interviews upon completion of training were conducted with 13 out of 15 participants. Interviews were analysed using directed content analysis and triangulated with quantitative data on secondary task reaction time for cognitive load estimation and participants' self-assessment scores.

Results: Six major themes were identified in the interviews: goal-directed behaviour, use of learning supports for scaffolding of the training, cognitive engagement, motivation from self-assessment, self-assessment bias, and feedback on self-assessment (validation). Participants seemed to self-regulate their learning by forming individual sub-goals and strategies within the overall goal of the procedure. They scaffolded their learning through the available learning supports. Finally, structured self-assessment was reported to increase the participants' cognitive engagement, which was further supported by a quantitative increase in cognitive load.

Conclusions: Structured self-assessment in simulation-based surgical training of mastoidectomy seems to promote cognitive engagement and motivation in the learning task and to facilitate self-regulated learning.