在小样本中估计IRT模型的监督学习方法
Dmitry I Belov1, Oliver Lüdtke2,3, Esther Ulitzsch2,4,5
1Law School Admission Council, Newtown, Pennsylvania, USA.
概括
一种用于对象响应理论 (IRT) 估计的新神经网络 (NN) 方法可以在不使用概率函数的情况下,在小样本中提高参数准确性. 这种方法比传统的贝叶斯技术提供了更快,更可靠的结果.
科学领域:
- 心理测量 心理测量 心理测量
- 机器学习 机器学习
- 教育测量教育的测量
背景情况:
- 项目响应理论 (IRT) 模型在教育和心理评估中被广泛使用.
- 传统的IRT参数估计依赖于概率函数,在小样本中可能不可靠,导致偏差估计和大标准误差.
- 需要强大的IRT估计方法,这些方法在有限的数据上表现良好.
研究的目的:
- 引入一种新的,无概率的方法来估计物品响应理论 (IRT) 模型参数.
- 开发和评估基于神经网络 (NN) 的小样本IRT估计方法.
- 为了证明NN方法对现有的贝叶斯估计技术的优势.
主要方法:
- 开发了一种新的估计方法,该方法从响应数据中提取特征,并使用神经网络 (NN) 将它们映射到项目参数中.
- 实施了三种类型的NN,以获得IRT参数的点估计和置信区间.
- 拟议的NN方法使用模拟研究进行了评估,将其性能与贝叶斯估计与马尔科夫链蒙特卡洛 (MCMC) 方法进行了比较.
主要成果:
- 与贝叶斯的MCMC方法相比,基于NN的方法在点估计和置信区间的质量方面表现优越.
- 该NN方法比传统的贝叶斯估计技术快得多.
- 模拟结果证实了NN方法对小样本IRT参数估计的有效性.
结论:
- 神经网络为项目响应理论 (IRT) 参数估计提供了强大而高效的替代方案,特别是在小样本大小中.
- 这种无概率的NN方法促进了实时项目预测,并提高了在线测试环境中新项目开发的安全性.
- 开发的NN方法提供了更高的准确性和速度,使它们在心理测量和教育测量的实际应用中具有价值.
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