计算机科学
新颖性
马尔科夫蒙特卡洛
机器学习
人工智能
期望最大化算法
最大化
项目反应理论
数据挖掘
心理测量学
最大似然
心理学
贝叶斯概率
数学优化
统计
数学
社会心理学
标识
DOI:10.3102/1076998607309474
摘要
Cognitive and skills diagnosis models are psychometric models that have immense potential to provide rich information relevant for instruction and learning. However, wider applications of these models have been hampered by their novelty and the lack of commercially available software that can be used to analyze data from this psychometric framework. To address this issue, this article focuses on one tractable and interpretable skills diagnosis model—the DINA model—and presents it didactically. The article also discusses expectation-maximization and Markov chain Monte Carlo algorithms in estimating its model parameters. Finally, analyses of simulated and real data are presented.
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