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中文摘要：
认知诊断（Cognitive diagnosis,CD）是智能教学系统的重要组成部分,但其研究存在一些问题：且对具有认知诊断功能的计算机化自适应测验（Computerized adaptive testing,CAT）的报导很少。CAT因人施测,公平高效,适合大规模测验。在规则空间模型与属性层次法的理论基础上,提出新的属性层次结构的类型,将形式概念分析（Formal concept analysis,FCA）应用于CD-CAT,以概念格为其理论模型,采用合适的题库建设技术、CD-CAT的选题策略和估计方法,建立概念格与CD-CAT的联系,据此开发出CD-CAT以实现对被试的实时诊断并讨论认知诊断的算法。采用Monte Carlo模拟进行实验,实验结果表明被试的知识状态较好地被诊断,能力估计精度在89%以上。
英文摘要：
Cognitive diagnosis （CD） is important part in the intelligent tutoring system. But some problems exist in CD： the diagnosis accuracy is low, when many cognitive attributes are involved in items. Few researches focus on computerized adaptive testing （CAT） with CD. CAT is adaptive, fair, and efficient, thus it is suitable for large-scale examinations. So CAT with cognitive diagnosis is prospective. Combined with Tatsuoka rule space model and the attribute hierarchy method （AHM）, formal concept analysis （FCA） is applied in CD-CAT. Taking a concept lattice is served as the model, and the technology of item bank construction, item selection strategies in CD-CAT and estimation method are used to design an initiatory and systemic CD-CAT, and it can diagnose examinees real time. The algorithms for diagnosing examinees are discussed. Monte Carlo simulation is used to simulate item bank, examinees and examinees responses. The result of Monte Carlo study shows that examinees knowledge states are well diagnosed and the precision in examinees abilities estimation is above 89%.
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