Applying prerequisite structure inference to adaptive testing
Applying prerequisite structure inference to adaptive testing
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DOI：
10.1145/3375462.3375541
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
S. Saarinen;Evan Cater;M. Littman
中科院分区：
文献类型：
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作者：
S. Saarinen;Evan Cater;M. Littman
学生知识建模对于评估设计、适应性测试、课程设计和教学干预非常重要。评估设计社区主要关注知识项之间具有强条件独立性假设的连续潜在技能模型，而先决条件发现社区开发了许多旨在利用离散知识项的相互依赖性的模型。本文试图通过以下问题来弥合这一差距：“评估项目相互依赖性建模何时可以提高预测准确性？”引入了一种新颖的自适应测试评估框架，该框架适用于两个社区的技术，并引入了一种有效的算法，即定向项目依赖性和置信度阈值（DIDACT），并在几个真实和合成数据集上与基于项目响应理论的模型进行了比较。实验表明，对密切相关的问题进行评估可以从项目相互依赖性建模中受益匪浅。
Modeling student knowledge is important for assessment design, adaptive testing, curriculum design, and pedagogical intervention. The assessment design community has primarily focused on continuous latent-skill models with strong conditional independence assumptions among knowledge items, while the prerequisite discovery community has developed many models that aim to exploit the interdependence of discrete knowledge items. This paper attempts to bridge the gap by asking, "When does modeling assessment item interdependence improve predictive accuracy?" A novel adaptive testing evaluation framework is introduced that is amenable to techniques from both communities, and an efficient algorithm, Directed Item-Dependence And Confidence Thresholds (DIDACT), is introduced and compared with an Item-Response-Theory based model on several real and synthetic datasets. Experiments suggest that assessments with closely related questions benefit significantly from modeling item interdependence.