非变量测量的潜伏马尔科夫模型:对计算机交互评估的交互日志数据的应用
1Department of Educational Psychology, https://ror.org/00hj54h04University of Texas at Austin, Austin, TX, USA.
Psychometrika
|August 26, 2025
概括
这项研究改进了潜在的马尔科夫模型 (LMM),以考虑计算机交互评估中的项目测量效应. 增强的LMM框架为大规模评估数据提供了更强大和更相关的推断.
科学领域:
- 心理测量
- 教育测量
- 统计模型
背景情况:
- 隐性马尔科夫模型 (LMM) 越来越多地用于分析来自计算机交互评估的日志数据.
- 目前的LMM应用通常假定单一的项目效应,忽视它们独特的心理测量特性和对结果差异的贡献.
研究的目的:
- 提出和评估一个改进的LMM,放松测量不变约束.
- 在评估数据分析中考虑特定事件的测量效应.
主要方法:
- 修改LMM以处理不变的测量.
- 推断方案的完善,以纳入特定事件的测量效应.
- 数字实验以验证推理方法和评估框架性能.
主要成果:
- 拟议的推断方案充分检索模型参数和状态配置文件.
- 精细的LMM框架在模拟潜伏过程中表现出可靠的性能.
- 与传统方法相比,新框架显示出更大的相关性,并产生更强大的推断,特别是当模型不精确时.
结论:
- 精确的LMM框架有效地考虑了评估数据中的项目测量效应.
- 这种新方法有助于改进具有明显测量效应的大规模评估数据的分析.
- 这些发现支持增强的LMM对更准确的心理评估的有用性.
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