A Framework for Multifaceted Evaluation of Student Models
A Framework for Multifaceted Evaluation of Student Models
复制标题
学生模型多方面评估框架
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Peter Brusilovsky
中科院分区：
文献类型：
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作者：
Yun Huang;José P. González;Rohit Kumar;Peter Brusilovsky
潜在变量模型，如流行的知识跟踪方法，经常被用来使自适应辅导系统能够个性化教育。然而，当考虑潜变量模型时，寻找最优模型参数通常是一个困难的非凸优化问题。以前的工作已经报道，从教育数据中获得的潜在变量模型在预测性能、似是而非和一致性方面存在差异。不幸的是，仍然没有对这些性质进行统一的定量测量。本文提出了一个通用的统一框架(我们称之为Polygon)来对学生模型进行多方面的评估。该框架综合考虑了上述三个维度，并为不同学生模型的定量比较提供了新的衡量标准。这些属性会影响辅导体验的有效性，而传统的预测绩效指标则无法做到这一点。本工作展示了我们在不同教学系统的数据集上将知识跟踪与最近的FeatureAware学生知识跟踪(FAST)模型进行比较的方法。我们的分析表明，FAST总体上改善了所研究的所有维度的知识追踪。
Latent variable models, such as the popular Knowledge Tracing method, are often used to enable adaptive tutoring systems to personalize education. However, finding optimal model parameters is usually a difficult non-convex optimization problem when considering latent variable models. Prior work has reported that latent variable models obtained from educational data vary in their predictive performance, plausibility, and consistency. Unfortunately, there are still no unified quantitative measurements of these properties. This paper suggests a general unified framework (that we call Polygon) for multifaceted evaluation of student models. The framework takes all three dimensions mentioned above into consideration and offers novel metrics for the quantitative comparison of different student models. These properties affect the effectiveness of the tutoring experience in a way that traditional predictive performance metrics fall short. The present work demonstrates our methodology of comparing Knowledge Tracing with a recent model called FeatureAware Student Knowledge Tracing (FAST) on datasets from different tutoring systems. Our analysis suggests that FAST generally improves on Knowledge Tracing along all dimensions studied.