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Applying Item Response Theory to Analyzing and Improving the Item Quality of an Online Chinese Reading Assessment
Conference paper

Applying Item Response Theory to Analyzing and Improving the Item Quality of an Online Chinese Reading Assessment

Xinyun Tian, Xiaoxue Han, Hercy N.H. Cheng, Wang-Chen Chang, Calvin C.Y. Liao, Jianwen Sun, Xiaoliang Zhu and Sanya Liu
Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, pp.754-759
11/2017

Abstract

Item response theory Reading ability Test quality Artificial Intelligence Computer Networks and Communications Computer Science Applications Information Systems Information Systems and Management
In order to realize the individualized teaching of Chinese language in primary schools, this research has developed an online Chinese reading assessment for primary schools, which aims to record the students test process and analyze the development level of students Chinese reading ability. In this paper, the item response theory (IRT) is applied to the quality analysis of the assessment in terms of measurement attributes (difficulty, discrimination, guessing), item characteristic curve, item information function and test information function. Additionally, this paper further explores the relationship between the various parameters of the items. The results show that the IRT can effectively guide the construction of the reading assessment scale, improve the discrimination degree, reduce the guessing degree, and effectively improve the quality of the item. This paper also proposes a method to find out the unreasonable options and modify items locally through project parameters and option analysis. It is expected that researchers and educators can modify the item more efficiently by quality analysis.

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