展开同行评分网络:一种潜在的可变方法
Giuseppe Mignemi1,2, Yunxiao Chen2, Irini Moustaki2
1Department of Decision Sciences, https://ror.org/05crjpb27Bocconi University, Milan, Italy.
Psychometrika
|June 16, 2025
概括
这项研究引入了贝叶斯隐性变量模型,用于同行评分数据,通过考虑分级器的变化来提高准确性. 该模型提供了更好的综合成绩和对教育环境中学年级可靠性的洞察.
科学领域:
- 教育技术的教育技术
- 统计建模 统计建模
- 贝叶斯的推理是贝叶斯的推理.
背景情况:
- 同行评分广泛用于大规模开放在线课程 (MOOC) 和传统教室.
- 它减少了教师的工作量,并通过积极参与增强了学生的学习.
- 由于互动的网络结构,同行评级数据表现出复杂的依赖关系.
研究的目的:
- 开发一个统计框架来分析复杂的同行评分数据.
- 为了解决由未建模的等级效应引起的聚合等级偏差.
- 提供一种评估个人评分表现和理解评分网络的方法.
主要方法:
- 引入潜在变量模型框架用于同行评分数据分析.
- 开发一个完全贝叶斯式的统计推理程序.
- 将模型应用于两个真实世界的同行评分数据集.
主要成果:
- 拟议的模型通过使用潜在变量来计算异质分级行为,从而产生更准确的总分.
- 它可以评估个体学生的成绩,识别可靠的评分员.
- 贝叶斯方法为模型参数和潜在变量提供了简单的不确定性量化方法.
结论:
- 隐性变量模型提供了一种强大的方法来分析同行评分数据,提高分数的准确性并提供有价值的见解.
- 这种方法提高了对同行评价系统中的评级者可靠性和学生绩效动态的理解.
- 贝叶斯框架确保可靠的推断和不确定性量化,使其适合教育数据分析.
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