Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates
Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates
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DOI：
10.3102/1076998617719727
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发表时间：
2018-02-01
影响因子：
2.4
通讯作者：
Douglas, Jeffrey A.
中科院分区：
文献类型：
--
作者：
Wang, Shiyu;Yang, Yan;Douglas, Jeffrey A.
提出了一种将认知诊断模型和高阶隐马尔可夫模型集成在一个框架中的学习模型族。这个新的框架包括协变量模型的学习环境中的技能过渡。采用贝叶斯公式来估计学习模型的参数。所开发的方法被应用到基于计算机的评估与学习干预。结果表明，该模型的潜在应用，以跟踪学生的技能的变化，直接提供即时补救，以及评估不同的干预措施的有效性，通过调查不同类型的学习干预措施如何影响从非掌握到掌握的过渡。
A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are applied to a computer-based assessment with a learning intervention. The results show the potential application of the proposed model to track the change of students' skills directly and provide immediate remediation as well as to evaluate the efficacy of different interventions by investigating how different types of learning interventions impact the transitions from nonmastery to mastery.