可比性
计算机科学
比例(比率)
数据收集
数据质量
人口
考试(生物学)
项目反应理论
数据科学
数据挖掘
心理测量学
统计
工程类
医学
数学
地理
组合数学
环境卫生
古生物学
公制(单位)
生物
地图学
运营管理
作者
Matthias von Davier,Lale Khorramdel,Qiwei He,Hyojeong Shin,Haiwen Chen
标识
DOI:10.3102/1076998619881789
摘要
International large-scale assessments (ILSAs) transitioned from paper-based assessments to computer-based assessments (CBAs) facilitating the use of new item types and more effective data collection tools. This allows implementation of more complex test designs and to collect process and response time (RT) data. These new data types can be used to improve data quality and the accuracy of test scores obtained through latent regression (population) models. However, the move to a CBA also poses challenges for comparability and trend measurement, one of the major goals in ISLAs. We provide an overview of current methods used in ILSAs to examine and assure the comparability of data across different assessment modes and methods that improve the accuracy of test scores by making use of new data types provided by a CBA.
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