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Using Bayesian Networks to Manage Uncertainty in Student Modeling

机译：使用贝叶斯网络管理学生建模中的不确定性

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摘要

When a tutoring system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. We use Bayesian networks as a comprehensive, sound formalism to handle this uncertainty. Using Bayesian networks, we have devised the probabilistic student models for Andes, a tutoring system for Newtonian physics whose philosophy is to maximize student initiative and freedom during the pedagogical interaction. Andes' models provide long-term knowledge assessment, plan recognition, and prediction of students' actions during problem solving, as well as assessment of students' knowledge and understanding as students read and explain worked out examples. In this paper, we describe the basic mechanisms that allow Andes' student models to soundly perform assessment and plan recognition, as well as the Bayesian network solutions to issues that arose in scaling up the model to a full-scale, field evaluated application. We also summarize the results of several evaluations of Andes which provide evidence on the accuracy of its student models.
机译：当辅导系统旨在为学生提供交互式帮助时，它需要知道学生所拥有的知识以及学生当前正在努力实现的目标。也就是说，它必须同时进行评估和计划认可。当允许学生遵循各种推理路线而无需明确显示所有推理时，这些建模任务会带来很大的不确定性。我们使用贝叶斯网络作为全面，合理的形式主义来处理这种不确定性。我们使用贝叶斯网络为安第斯山脉设计了概率学生模型，这是牛顿物理学的补习系统，其哲学是在教学互动中最大程度地提高学生的主动性和自由度。安第斯山脉的模型提供了长期的知识评估，计划识别以及在解决问题过程中对学生行为的预测，以及在学生阅读和解释实例后对学生的知识和理解进行评估。在本文中，我们描述了允许安第斯山脉学生模型正确执行评估和计划识别的基本机制，以及贝叶斯网络解决方案，以解决将模型扩展到全面的现场评估应用程序时出现的问题。我们还总结了对安第斯山脉的几次评估结果，这些评估结果证明了其学生模型的准确性。

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