Pusic Martin V, Boutis Kathy, Pecaric Martin R, Savenkov Oleksander, Beckstead Jason W, Jaber Mohamad Y
Institute for Innovations in Medical Education, New York University School of Medicine, 550 First Avenue, MSB G109, New York, NY, 10016, USA.
The Hospital for Sick Children, and University of Toronto, Toronto, ON, Canada.
Adv Health Sci Educ Theory Pract. 2017 Aug;22(3):741-759. doi: 10.1007/s10459-016-9709-2. Epub 2016 Oct 3.
Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and group basis with the resulting estimates being useful to both learners and educators for feedback and educational quality improvement. In this primer, we review various descriptive and modelling techniques appropriate to learning curves including smoothing, regression modelling and application of the Thurstone model. Using an example dataset we demonstrate each technique as it specifically applies to learning curves and point out limitations.
学习曲线是表示随时间学习速率的一种有用方式。其特征包括基线表现指数（y轴截距）、随时间的学习效率（斜率参数）以及可达到的最大理论表现（上渐近线）。这些参数中的每一个都可以在个体和群体基础上进行统计建模，所得估计值对学习者和教育工作者进行反馈及提高教育质量都很有用。在本入门介绍中，我们回顾了适用于学习曲线的各种描述性和建模技术，包括平滑处理、回归建模以及瑟斯顿模型的应用。我们使用一个示例数据集展示了每种技术具体如何应用于学习曲线，并指出其局限性。
