Algorithmic Debugging to Support Cognitive Diagnosis in Tutoring Systems
Algorithmic Debugging to Support Cognitive Diagnosis in Tutoring Systems
复制标题
支持辅导系统中认知诊断的算法调试
DOI：
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发表时间：
2011
期刊：
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通讯作者：
C. Zinn
中科院分区：
文献类型：
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作者：
C. Zinn
智能教学系统中的认知建模旨在从学习者对辅导问题的回答和其他观察到的行为中识别学习者的技能和知识。在本文中，我们提出了一种创新的Shapiro算法调试技术，它的应用可以用来根据专家模型的执行轨迹的不可约分歧来定位学习者的错误行为。我们的变体有两个主要的好处：与传统的方法相比，它不依赖于对错误规则的显式编码，第二，它诱导了一种自然的师生对话，不需要事先编写个别话轮的脚本或更高级别的对话计划。
Cognitive modelling in intelligent tutoring systems aims at identifying a learner's skills and knowledge from his answers to tutor questions and other observed behaviour. In this paper, we propose an innovative variant of Shapiro's algorithmic debugging technique whose application can be used to pin-point learners' erroneous behaviour in terms of an irreducible disagreement to the execution trace of an expert model. Our variant has two major benefits: in contrast to traditional approaches, it does not rely on an explicit encoding on mal-rules, and second, it induces a natural teacher-learner dialogue with no need for the prior scripting of individial turns or higher-level dialogue planning.