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Multilevel IRT models for the university teaching evaluation

机译:用于大学教学评估的多层IRT模型

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In this paper, a generalization of the two-parameter partial credit model (2PL-PCM) and of two special cases, the partial credit model (PCM) and the rating scale model (RSM), with a hierarchical data structure will be presented. Having shown how 2PL-PCM, as with other item response theory (IRT) models, may be read in terms of a generalized linear mixed model (GLMM) with two aggregation levels, a presentation will be given of an extension to the case of measuring the latent trait of individuals aggregated in groups. The use of this Multilevel IRT model will be illustrated via reference to the evaluation of university teaching by students following the courses. The aim is to generate a ranking of teaching on the basis of student satisfaction, so as to give teachers, and those responsible for organizing study courses, a background of information that takes the opinions of the direct target group for university teaching (that is, the students) into account, in the context of improving the teaching courses available.
机译:在本文中,将对两参数部分信用模型(2PL-PCM)以及两种特殊情况(部分信用模型(PCM)和评级量表模型(RSM))进行概括,并使用分层数据结构。展示了如何使用具有两个聚合级别的广义线性混合模型(GLMM)来阅读2PL-PCM和其他项目响应理论(IRT)模型一样的内容,下面将对测量情况进行扩展个人聚集在一起的潜在特征。通过参考课程后学生对大学教学的评估,将说明该多级IRT模型的用法。目的是在学生满意度的基础上对教学进行排名,从而为教师和负责组织学习课程的人员提供一种信息背景,该信息背景吸收了大学教学的直接目标群体的意见(即,学生),以改善现有的教学课程。

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