一个诊断方面状态模型 (DFSM) 用于从诊断评估中提取教学上有用的信息
1312E Miller Hall Measurement and Statistics, College of Education, University of Washington, 2012 Skagit Ln, Seattle, WA, 98105, USA. wang4066@uw.edu.
Psychometrika
|April 28, 2024
概括
本研究介绍了用于教育评估的诊断面状况模型 (DFSM). DFSM通过分析所有响应选项来增强认知诊断模型,以更好地了解学生的学习途径.
科学领域:
- 教育测量教育的测量
- 心理测量建模 心理测量建模
- 认知诊断模型 认知诊断模型
背景情况:
- 现代教育改革需要先进的诊断测试,以确定学生对学习目标和中间技能的理解.
- 对评估结果的准确解释对于有针对性的教学干预至关重要.
- 现有的心理测量模型需要加强细微的诊断能力.
研究的目的:
- 开发一种新的心理测量模型,即诊断面状况模型 (DFSM),以改善教育评估.
- 同时模拟学生的目标理解 (目标方面) 和中间理解 (中间方面).
- 将所有响应选项纳入,而不仅仅是正确/不正确,以获得更丰富的诊断信息.
主要方法:
- 开发了诊断面状况模型 (DFSM),是一种认知诊断模型的扩展.
- 采用规范期望最大化算法 (REM) 来进行模型估计,并包含对稀疏性的日志惩罚.
- 提出了一个启发式算法,从估计的允许隐性类推断一个面图.
主要成果:
- 规范期望最大化算法有效地估计了模型参数,并在模拟研究中确定了允许的潜在类.
- 强加的日志处罚成功地将零混合比例分配给不可允许的潜在类.
- 实际数据分析证明了诊断面状况模型的实际可行性.
结论:
- 诊断面状况模型为教育评估中的认知诊断提供了一种更细致的方法.
- 开发的规范期望最大化算法为估计新模型提供了一个强大的方法.
- 这种模型有助于更深入地了解学生的学习过程,从而实现更有效的教学准.
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