Using Learning Curves to Identify and Explain Growth Patterns of Learners in Bronchoscopy Simulation: A Mixed-Methods Study
Briseida Mema, Maria Mylopoulos, Ara Tekian, Yoon Soo Park
Abstract
PURPOSE: Learning curves can illustrate how trainees acquire skills and the path to competence. This study examined the growth trajectories of novice trainees while practicing on a bronchoscopy virtual reality (VR) simulator compared with those of experts. METHOD: This was a sequential explanatory mixed-methods design. Twenty pediatric subspecialty trainees and 7 faculty practiced with the VR simulator (October 2017 to March 2018) at the Hospital for Sick Children, Toronto, Canada. The authors examined the relationship between number of repetitions and VR outcomes and patterns of growth using a growth mixture modeling. Using an instrumental case study design, field notes and semistructured interviews with trainees and simulation instructor were examined to explain the patterns of growth. The authors used a constant comparative approach to identify themes iteratively. Team analysis continued until a stable thematic structure was developed and applied to the entire data. RESULTS: The growth mixture model identified 2 patterns of growth. A slower growth included learners that 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 growth included learners who used an adaptive expertise approach: integrating knowledge of anatomy, finding flexible solutions, and creating a deeper conceptual understanding. CONCLUSIONS: The authors 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.