Option Tracing: Beyond Correctness Analysis in Knowledge Tracing
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing
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DOI：
10.1007/978-3-030-78292-4_12
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Aritra Ghosh;Jay Raspat;Andrew S. Lan
中科院分区：
文献类型：
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作者：
Aritra Ghosh;Jay Raspat;Andrew S. Lan
知识追踪指的是一系列方法，根据每个学生过去对问题的回答来估计他们的知识成分/技能掌握水平。现有的大多数知识跟踪方法的一个关键局限性是，它们只能根据知识成分/技能来估计学生的整体知识水平，因为它们只分析学生回答的正确性(通常是二进制的)。因此，很难使用它们来诊断特定的学生错误。在本文中，我们将现有的知识追踪方法从正确率预测扩展到预测学生在多项选择题中选择的准确选项。我们在两个大规模的学生回答数据集上对我们的选项跟踪方法的性能进行了定量评估。我们还定性地评估了他们在识别常见学生错误方面的能力，这些错误是通过对应于同一错误的不同问题上的错误选项簇的形式来实现的。
Knowledge tracing refers to a family of methods that estimate each student’s knowledge component/skill mastery level from their past responses to questions. One key limitation of most existing knowledge tracing methods is that they can only estimate anoverallknowledge level of a student per knowledge component/skill since they analyze only the (usually binary-valued) correctness of student responses. Therefore, it is hard to use them to diagnose specific student errors. In this paper, we extend existing knowledge tracing methods beyond correctness prediction to the task of predicting the exact option students select in multiple choice questions. We quantitatively evaluate the performance of our option tracing methods on two large-scale student response datasets. We also qualitatively evaluate their ability in identifying common student errors in the form of clusters of incorrect options across different questions that correspond to the same error.