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Item Response Theory for Peer Assessment

机译：对等评估的项目反应理论

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摘要

As an assessment method based on a constructivist approach, peer assessment has become popular in recent years. However, in peer assessment, a problem remains that reliability depends on the rater characteristics. For this reason, some item response models that incorporate rater parameters have been proposed. Those models are expected to improve the reliability if the model parameters can be estimated accurately. However, when applying them to actual peer assessment, the parameter estimation accuracy would be reduced for the following reasons. 1) The number of rater parameters increases with two or more times the number of raters because the models include higher-dimensional rater parameters. 2) The accuracy of parameter estimation from sparse peer assessment data depends strongly on hand-tuning parameters, called hyperparameters. To solve these problems, this article presents a proposal of a new item response model for peer assessment that incorporates rater parameters to maintain as few rater parameters as possible. Furthermore, this article presents a proposal of a parameter estimation method using a hierarchical Bayes model for the proposed model that can learn the hyperparameters from data. Finally, this article describes the effectiveness of the proposed method using results obtained from a simulation and actual data experiments.
机译：作为一种基于建构主义方法的评估方法，同伴评估在最近几年变得很流行。但是，在同peer评估中，仍然存在可靠性取决于评估者特征的问题。因此，已经提出了一些包含评估者参数的项目响应模型。如果可以准确地估计模型参数，则这些模型有望提高可靠性。但是，将它们应用于实际的对等评估时，由于以下原因会降低参数估计的准确性。 1）评估者参数的数量以评估者数量的两倍或更多倍增加，因为模型包含更高维度的评估者参数。 2）从稀疏的对等评估数据估计参数的准确性在很大程度上取决于称为超参数的手动调整参数。为了解决这些问题，本文提出了一种用于对等评估的新项目响应模型的建议，该模型结合了评估者参数以保持尽可能少的评估者参数。此外，本文提出了一种使用分层贝叶斯模型的参数估计方法的建议，该模型可以从数据中学习超参数。最后，本文使用从仿真和实际数据实验获得的结果来描述所提出方法的有效性。

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