进度监测决策规则的准确性,以评估对指令的响应,使用两个计算机自适应测试
Ethan R Van Norman1, Emily R Forcht1
1Center for Promoting Research to Practice, Lehigh University, United States of America.
Journal of school psychology
|June 14, 2024
概括
计算机适应性测试,如星星阅读和星星数学,需要每月进行许多评估,以准确监测进度. 需要七到十个数据点,这表明替代方法可能对指令决策更有效.
科学领域:
- 教育测量教育的测量
- 学习分析学习分析
- 学术进步监测 学术进步监测
背景情况:
- 计算机适应性测试 (CAT) 越来越多地用于选具有学术风险的学生.
- 新兴的研究探讨了CAT在进度监测和对指令的响应方面的实用性.
- 在CAT中,趋势线决策规则的准确性需要进一步评估.
研究的目的:
- 评估星读 (SR) 和星数学 (SM) CATs趋势线决策规则的准确性.
- 确定需要的评估数量,以确保进展监测的敏感性和特异性足够.
- 将绩效与基准目标线和规范百分位数进行比较.
主要方法:
- 分析现存的星星阅读和星星数学数据,来自美国西北部的三年级到五年级的学生.
- 进行模拟以确定所需的每月评估次数.
- 灵敏度和特异性是基于趋势线决定与目标线和百分位相比计算的.
主要成果:
- 对于星星阅读,分别需要为3年级,4年级和5年级进行7,8和9个月的观测.
- 对于星星数学,分别需要9次,10次和8次月度观测,分别为3年级,4年级和5年级.
- 需要观察的数量表明,要做出可靠的决策,需要一个漫长的过程.
结论:
- 需要大量的每月评估才能可靠地使用SR和SM的趋势线规则来监测进展.
- 所需的时间投资可能会限制这些CAT用于频繁的进度监测的实用性.
- 建议考虑其他评估工具和决策框架,以有效监测学术进步.
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