A Multivariate Probit Model for Learning Trajectories: A Fine-Grained Evaluation of an Educational Intervention
A Multivariate Probit Model for Learning Trajectories: A Fine-Grained Evaluation of an Educational Intervention
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学习轨迹的多元概率模型：教育干预的细粒度评估
DOI：
10.1177/0146621620920928
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发表时间：
2020-06-06
影响因子：
1.2
通讯作者：
Culpepper, Steven Andrew
中科院分区：
文献类型：
--
作者：
Chen, Yinghan;Culpepper, Steven Andrew
教育技术的进步为教师和学校提供了有关学生表现的丰富信息。教育研究的一个重要方向是收集现有的纵向数据，为教师提供关于学生技能掌握的实时诊断。认知诊断模型（CDMs）为教育研究人员、政策制定者和从业者提供了一个心理测量框架，用于设计与教学相关的评估和诊断学生的技能概况。在这篇文章中，作者通过提出一个多变量潜在增长曲线模型来描述学生随时间的学习轨迹，为纵向cdm的发展做出了贡献。这种模式有几个优点。首先，学习轨迹空间是高维的，以前开发的模型可能不适用于中等样本量的教育研究。相比之下，该方法提供了较低维度的近似，更适用于典型的教育研究。其次，从业者和研究人员对确定导致或与学生技能习得相关的因素感兴趣。该框架可以很容易地结合协变量来评估有关促进学习的因素的理论问题。作者通过应用于测试前或测试后的教育干预研究，展示了他们的方法的实用性，并展示了纵向CDM框架如何提供对实验效果的细粒度评估。
Advances in educational technology provide teachers and schools with a wealth of information about student performance. A critical direction for educational research is to harvest the available longitudinal data to provide teachers with real-time diagnoses about students' skill mastery. Cognitive diagnosis models (CDMs) offer educational researchers, policy makers, and practitioners with a psychometric framework for designing instructionally relevant assessments and diagnoses about students' skill profiles. In this article, the authors contribute to the literature on the development of longitudinal CDMs, by proposing a multivariate latent growth curve model to describe student learning trajectories over time. The model offers several advantages. First, the learning trajectory space is high-dimensional and previously developed models may not be applicable to educational studies that have a modest sample size. In contrast, the method offers a lower dimensional approximation and is more applicable for typical educational studies. Second, practitioners and researchers are interested in identifying factors that cause or relate to student skill acquisition. The framework can easily incorporate covariates to assess theoretical questions about factors that promote learning. The authors demonstrate the utility of their approach with an application to a pre- or post-test educational intervention study and show how the longitudinal CDM framework can provide fine-grained assessment of experimental effects.