B. Mema is a staff physician in the Department of Critical Care Medicine, Hospital for Sick Children and Associate Professor in the Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, Canada.
M. Mylopoulos is a scientist at the Wilson Center and Associate Professor in the Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, Canada.
Acad Med. 2020 Dec;95(12):1921-1928. doi: 10.1097/ACM.0000000000003595.
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.
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.
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.
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.
学习曲线可以说明学员如何掌握技能以及达到熟练程度的路径。本研究比较了新手学员在支气管镜虚拟现实（VR）模拟器上练习时的增长轨迹与专家的增长轨迹。
这是一项序贯解释性混合方法设计。20 名儿科专科培训师和 7 名教师于 2017 年 10 月至 2018 年 3 月在加拿大多伦多 SickKids 医院使用 VR 模拟器进行练习。作者使用增长混合建模来检查重复次数与 VR 结果之间的关系以及增长模式。使用仪器案例研究设计，检查学员和模拟指导员的现场记录和半结构化访谈，以解释增长模式。作者使用恒定比较方法迭代地确定主题。团队分析一直持续到开发并应用于整个数据的稳定主题结构。
增长混合模型确定了 2 种增长模式。较慢的增长模式包括那些具有固有技能困难、无法在模拟实践中整合解剖学知识、并且没有策略在试验之间进行改进而仅进行简单重复练习的学习者。较快的增长模式包括那些使用适应性专长方法的学习者：整合解剖学知识、寻找灵活的解决方案，并建立更深入的概念理解。
作者为教育中使用增长模型提供了有效性证据，并解释了增长模式，例如具有机械重复练习的“缓慢增长”和具有适应性专长的“快速增长”。
