使用深度学习来选择最佳平滑值
1Psychometrics and Data Analysis, National Board of Medical Examiners, Philadelphia, PA, USA.
Applied psychological measurement
|August 29, 2025
概括
这项研究使用深度学习实现了自动化测试成绩等同. 一个卷积神经网络与人类专家在选择测试形式等同的最佳平滑值方面达成了71%的协议.
科学领域:
- 心理测量
- 机器学习
- 教育测量
背景情况:
- 为了保持测试分数的完整性,使用了替代测试形式.
- 由于难度的差异,不同测试形式之间的分数被调整为等级.
- 在等式化过程中应用平滑方法以尽量减少采样错误.
研究的目的:
- 在测试中自动选择最佳的光滑值.
- 评估深度学习,特别是卷积神经网络 (CNN) 的有效性.
- 为了比较CNN的性能与人类的专家判断在选择平滑参数.
主要方法:
- 一个卷积神经网络被训练在人类分类的后滑图.
- 经过训练的CNN被用来确定经验测试数据的最佳平滑值.
- 美国有线电视新闻网的选择与人类专家的选择进行了比较.
主要成果:
- 深度学习模型与人类专家达成了71%的共识.
- 这表明自动化方法与手动选择之间存在很高的一致性.
结论:
- 深度学习提供了一种可行的自动化方法,用于在测试中选择最佳的平滑值.
- 这种自动化有可能提高等效过程的效率和一致性.
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