一个新的适合人群的统计数据,用于在多种性认知诊断模型中检测异常反应
Xuliang Gao1, Minmin Hou2, Fang Wang2
1School of Psychology, Guizhou Normal University, Huaxi University Town, Guian New District, Guiyang, 550025, Guizhou Province, China. gaoxl9817@foxmail.com.
Behavior research methods
|April 9, 2025
概括
一个新的适合人群的统计数据,WR,有效地识别了多种物体的认知诊断评估中的异常反应. WR的表现优于传统方法,特别是在低质量的测试环境中,提高了准确的学生属性分析.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 认知诊断模型 (CDM) 是一种认知诊断模型.
背景情况:
- 准确的个人适应性评估在CDM中至关重要,以防止对学生属性配置文件的误解.
- 现有的个人适应统计数据对于CDM中的多种类型项目缺乏具体应用.
- 错误的反应可能导致不适当的教育干预.
研究的目的:
- 引入和评估一个新的个人适应统计,WR,针对CDM中的多种类型项目量身定制.
- 评估WR在检测各种异常反应模式中的有效性.
- 为了比较WR的表现与已建立的个人适应统计数据.
主要方法:
- 开发多种类型的CDM的WR人体适应统计.
- 模拟研究来评估WR的欺骗和创造性反应检测能力.
- 在不同的条件下,WR与传统统计数据 (lz,infit,outfit) 的比较.
- 分析实际的教育评估数据,以证明其实际应用.
主要成果:
- 在所有模拟场景中,WR表现出稳定且优异的不适合人群检测能力.
- 传统的方法表现不一致,lz更适合作弊,infit更适合创造性反应.
- 在低质量的测试环境中,WR显著优于传统方法.
- 在高质量的环境中,WR的性能与传统方法相美.
结论:
- WR统计是一个有效的工具,用于检测多种分数CDM中不适合的人.
- 通过识别不合适的响应,WR提高了学生属性分析的准确性.
- 该研究通过真实世界的数据分析验证了WR的实际实用性.
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