为估计平均能力增长的最佳测试设计
Jonas Bjermo1,2
1Linköping University, Sweden.
Applied psychological measurement
|November 15, 2024
概括
本研究提出了一种最佳的测试设计方法,以精确估计学年级之间的学生能力增长. 粒子群集优化用于分配项目难度,提高增长测量的准确性.
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
- 统计 统计 统计 统计
背景情况:
- 准确测量学生能力增长对于教育评估至关重要.
- 测试设计,特别是项目难度分配,显著影响测量精度.
- 现有的方法在估计不同人群的能力增长时可能缺乏稳定性.
研究的目的:
- 引入一个最佳的测试设计方法,以高精度估计平均和百分点能力增长.
- 调查项目歧视和能力增长大小对最佳项目难度分配的影响.
- 评估拟议设计的稳定性,以应对平均能力增长的不确定性.
主要方法:
- 使用与测试信息相关的异常的非对称表达式进行优化.
- 采用粒子群优化 (PSO) 来确定项目难度的最佳分配.
- 实现离散优化,使用模拟回火进行实践测试,从项目池中组装.
主要成果:
- 最佳的项目难度分配取决于项目歧视和能力增长的大小.
- 优化函数对受试者的能力的依赖,需要考虑未知的平均能力增长.
- 一个最佳的平均设计证明了在平均能力增长的不确定性面前的稳定性.
结论:
- 粒子群优化为设计成就测试提供了一种有效的方法,以测量能力增长.
- 拟议的测试设计方法提高了估计平均和百分点能力增长的精度.
- 该研究强调了考虑项目参数和人群特征对于可靠的测试设计的重要性.
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