A Hidden Markov Model for Learning Trajectories in Cognitive Diagnosis With Application to Spatial Rotation Skills
A Hidden Markov Model for Learning Trajectories in Cognitive Diagnosis With Application to Spatial Rotation Skills
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DOI：
10.1177/0146621617721250
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发表时间：
2018-01-01
影响因子：
1.2
通讯作者：
Douglas, Jeffrey
中科院分区：
文献类型：
--
作者：
Chen, Yinghan;Culpepper, Steven Andrew;Douglas, Jeffrey
越来越多的电子和在线学习资源的存在提出了挑战和机会，心理测量技术，可以帮助测量的能力，甚至加快他们的掌握。认知诊断模型（CDM）是跟踪组成一个领域的许多细粒度技能的理想选择，并且可以帮助在电子学习应用程序中仔细导航这些技能的培训和评估。提出了一类用于属性变化建模的CDM，称为学习轨迹。作者专注于发展贝叶斯程序估计参数的一阶隐马尔可夫模型。所开发的模型的空间旋转实验干预的应用。
The increasing presence of electronic and online learning resources presents challenges and opportunities for psychometric techniques that can assist in the measurement of abilities and even hasten their mastery. Cognitive diagnosis models (CDMs) are ideal for tracking many fine-grained skills that comprise a domain, and can assist in carefully navigating through the training and assessment of these skills in e-learning applications. A class of CDMs for modeling changes in attributes is proposed, which is referred to as learning trajectories. The authors focus on the development of Bayesian procedures for estimating parameters of a first-order hidden Markov model. An application of the developed model to a spatial rotation experimental intervention is presented.