印为红字的
拉什模型
构造(python库)
一致性(知识库)
差异项目功能
知识整合
度量(数据仓库)
结构效度
心理学
项目反应理论
项目分析
心理测量学
计算机科学
数学教育
人工智能
数据挖掘
领域知识
发展心理学
程序设计语言
作者
Ou Lydia Liu,Hee‐Sun Lee,Carolyn Huie Hofstetter,Marcia C. Linn
标识
DOI:10.1080/10627190801968224
摘要
In response to the demand for sound science assessments, this article presents the development of a latent construct called knowledge integration as an effective measure of science inquiry. Knowledge integration assessments ask students to link, distinguish, evaluate, and organize their ideas about complex scientific topics. The article focuses on assessment topics commonly taught in 6th- through 12th-grade classes. Items from both published standardized tests and previous knowledge integration research were examined in 6 subject-area tests. Results from Rasch partial credit analyses revealed that the tests exhibited satisfactory psychometric properties with respect to internal consistency, item fit, weighted likelihood estimates, discrimination, and differential item functioning. Compared with items coded using dichotomous scoring rubrics, those coded with the knowledge integration rubrics yielded significantly higher discrimination indexes. The knowledge integration assessment tasks, analyzed using knowledge integration scoring rubrics, demonstrate strong promise as effective measures of complex science reasoning in varied science domains.
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