Learning Curves: Identifying and Explaining Growth Trajectory of Learners in Bronchoscopy Simulation. A Mixed Method Study

PURPOSE: Learning curves show how trainees acquire a skill. Unlike other forms of assessment that show whether the learning has occurred, learning curves show what the path to competence looks like. The aim of this study was to describe the learning curves of novice trainees while practicing on a Bronchoscopy Virtual Reality (VR) simulator and identify and explain patterns that may alert educators of trainees in difficulty. METHODS: This was a sequential explanatory mixed methods design. In 2018, 20 Pediatric Critical Care and Respirology Subspeciality trainees as well as eight faculty practiced with the VR simulator. We looked at relationship between number of repetitions and VR outcomes and patterns of growth using a growth mixture modeling. Using a qualitative instrumental case study method we collected field notes and conducted semi-structured interviews with trainees and simulation instructor to explore the use of automatic scoring from the VR simulator, pattern of practice and strategies used for learning during the simulation practice. Constant comparative analysis was used to identify themes iteratively. Team analysis continued until a stable thematic structure was developed and applied to the entire data set. RESULTS: Using a growth mixture modeling we statistically identified two patterns of growth. Eight out of twenty learners belonged to the group with a slower growth and plateau at a lower score. The field notes and interviews identified five out of eight learners in that group as the ones who had: inherent difficulty with the skill, did not integrate the knowledge of anatomy in simulation practice and used the simulator for simple repetitive practice with no strategy for improvement in between trials. The faster growing group used strategies of adaptive expertise. CONCULSIONS: We provide validity evidence for use of growth models in education and explain patterns of growth such as a “slow growth” with a mechanistic repetitive practice and a “fast growth” with adaptive expertise.