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首页> 外文期刊>Frontiers in Psychology >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：一种用于动态监测计算机实践环境的能力增长的多维IRT方法

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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. 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：自适应学习系统已获得越来越多的关注，因为它们可以提供针对个别学习者的行为和需求量身定制的个性化指示。为了达到这个目标，希望有一个评估系统，监控每个学习者的能力实时变化。 ELO评级系统（ERS）是一种配对竞争的流行评分算法，最近被认为是一种快速而灵活的方法，可以评估在线学习环境中的学习进度。然而，已经认为，由于嵌入了学习材料中的能力的多维性质，标准的分别可能是有问题的。为了处理此问题，我们提出了一个系统，该系统包含在ERS中的多维项目响应理论模型（MIRT）。基本思想是，我们的方法而不是从RASCH模型更新单个能力参数，而是允许基于补偿MIRT模型同时更新多个能力参数，从而导致对“M-ERS”的多维扩展。为了评估方法，进行了三种模拟研究。结果表明，与M-ERS相比，错误地假设单向标志性的人具有严重降低的预测准确性。在M-ERS中占用速度和准确性的算法显示比仅使用精度数据更好。应用进一步说明了使用来自流行的教育平台的现实生活数据来锻炼数学技能的方法。

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