Department of Educational Psychology, University of Georgia.
Department of Statistics, Columbia University.
Multivariate Behav Res. 2020 Jan-Feb;55(1):49-68. doi: 10.1080/00273171.2019.1607238. Epub 2019 Jun 5.
A general modeling framework of response accuracy and response times is proposed to track skill acquisition and provide additional diagnostic information on the change of latent speed in a learning environment. This framework consists of two types of models: a dynamic response model that captures the response accuracy and the change of discrete latent attribute profile upon factors such as practice, intervention effects, and other latent and observable covariates, and a dynamic response time model that describes the change of the continuous response latency due to change of latent attribute profile. These two types of models are connected through a parameter, describing the change rate of the latent speed through the learning process, and a covariate defined as a function of the latent attribute profile. A Bayesian estimation procedure is developed to calibrate the model parameters and measure the latent variables. The estimation algorithm is evaluated through several simulation studies under various conditions. The proposed models are applied to a real data set collected through a spatial rotation diagnostic assessment paired with learning tools.
提出了一种通用的反应准确性和反应时间建模框架，以跟踪技能获取，并提供学习环境中潜在速度变化的额外诊断信息。该框架由两种类型的模型组成：一种动态反应模型，用于捕获反应准确性和离散潜在属性分布的变化，这些变化受到实践、干预效果以及其他潜在和可观察协变量的影响；另一种动态反应时间模型，用于描述由于潜在属性分布的变化而导致的连续反应潜伏期的变化。这两种类型的模型通过一个参数连接起来，该参数描述了通过学习过程中潜在速度的变化率，以及一个协变量，该协变量定义为潜在属性分布的函数。开发了一种贝叶斯估计程序来校准模型参数并测量潜在变量。通过在各种条件下进行的几项模拟研究评估了估计算法。所提出的模型应用于通过与学习工具配对的空间旋转诊断评估收集的真实数据集。
