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在瑞典学术能力测试的背景下最佳项目校准
Jonas Bjermo1,2, Ellinor Fackle Fornius2, Frank Miller1
1Department of Computer and Information Science, Linköping University, Linköping, Sweden.
Applied psychological measurement
|February 9, 2026
概括
在大规模测试中优化项目校准可提高精度. 这项研究表明,以能力为依赖的预测项的分配提高了对未来成就测试的项目特征估计.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 测试理论 测试理论
背景情况:
- 大规模的成就测试依赖于项目银行来进行未来的测试开发.
- 通过项目校准准确估计项目特性对于测试质量至关重要.
- 最佳设计方法旨在提高项目校准的效率.
研究的目的:
- 调查预测项目的最佳能力依赖分配的实际性能.
- 用这种方法在现实环境中量化校准精度的增长.
- 为了确定从最佳校准中获益最多的特定项目类型.
主要方法:
- 应用最佳设计原则,以对能力依赖的预测项目进行分配.
- 使用瑞典学术能力测试 (SweSAT) 的数据进行实证调查.
- 对校准精度的分析和影响方法性能的因素的识别.
主要成果:
- 能力依赖的分配通常会提高项目校准的精度.
- 在所有项目中,精度的平均改善均为中等.
- 确定了特定的项目类型,其中最佳分配方法特别有效.
结论:
- 优化能力依赖分配是提高项目校准在大规模测试中的可行策略.
- 该方法提供了有针对性的改进,使资源的使用更有效.
- 该方法的进一步改进可以为测试开发带来更大的好处.
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