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首页> 美国卫生研究院文献>Frontiers in Public Health >A Multidimensional IRT Approach for Dynamically Monitoring Ability Growth in Computerized Practice Environments
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A Multidimensional IRT Approach for Dynamically Monitoring Ability Growth in Computerized Practice Environments

机译：动态监控计算机实践环境中能力增长的多维IRT方法

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摘要

Adaptive learning systems have received an increasing attention as they enable to provide personalized instructions tailored to the behaviors and needs of individual learners. In order to reach this goal, it is desired to have an assessment system, monitoring each learner's ability change in real time. The Elo Rating System (ERS), a popular scoring algorithm for paired competitions, has recently been considered as a fast and flexible method that can assess learning progress in online learning environments. However, it has been argued that a standard ERS may be problematic due to the multidimensional nature of the abilities embedded in learning materials. In order to handle this issue, we propose a system that incorporates a multidimensional item response theory model (MIRT) in the ERS. The basic idea is that instead of updating a single ability parameter from the Rasch model, our method allows a simultaneous update of multiple ability parameters based on a compensatory MIRT model, resulting in a multidimensional extension of the ERS (“M-ERS”). To evaluate the approach, three simulation studies were conducted. Results suggest that the ERS that incorrectly assumes unidimensionality has a seriously lower prediction accuracy compared to the M-ERS. Accounting for both speed and accuracy in M-ERS is shown to perform better than using accuracy data only. An application further illustrates the method using real-life data from a popular educational platform for exercising math skills.
机译：自适应学习系统越来越受到关注，因为它们能够提供针对个别学习者的行为和需求量身定制的个性化指导。为了达到这个目标，希望有一个评估系统，实时监测每个学习者的能力变化。 Elo评分系统（ERS）是一种用于配对比赛的流行评分算法，最近被认为是一种可以评估在线学习环境中学习进度的快速灵活的方法。但是，有人争辩说，由于嵌入在学习材料中的能力的多维性质，标准ERS可能会出现问题。为了解决此问题，我们提出了一个在ERS中包含多维项目响应理论模型（MIRT）的系统。基本思想是，我们的方法允许根据补偿MIRT模型同时更新多个能力参数，而不是从Rasch模型中更新单个能力参数，从而导致ERS（“ M-ERS”）的多维扩展。为了评估该方法，进行了三个仿真研究。结果表明，与M-ERS相比，错误地假设为一维的ERS的预测准确性严重降低。与仅使用准确性数据相比，在M-ERS中兼顾速度和准确性显示出更好的性能。应用程序进一步说明了使用来自流行教育平台的真实数据来锻炼数学技能的方法。

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