Automated Prediction of Item Difficulty in Reading Comprehension Using Long Short-Term Memory

被引：0

作者：

Lin, Li-Huai ^{[1
]}

Chang, Tao-Hsing ^{[1
]}

Hsu, Fu-Yuan ^{[2
]}

机构：

[1] Natl Kaohsiung Univ Sci & Technol, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan

[2] Natl Taiwan Normal Univ, Inst Res Excellence Learning Sci, Taipei, Taiwan

来源：

PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP) | 2019年

关键词：

sentence Reading comprehension; item difficulty estimation; Long short-term memory;

D O I：

10.1109/ialp48816.2019.9037716

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Standardized tests are an important tool in education. During the test preparation process, the difficulty of each test item needs to be defined, which previously relied on expert validation or pretest for the most part, requiring a considerable amount of labor and cost. These problems can be overcome by using machines to predict the difficulty of the test items. In this study, long short-term memory (LSTM) will be used to predict the test item difficulty in reading comprehension. Experimental results show that the proposed method has a good prediction for agreement rate.

引用

页码：132 / 135

页数：4

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