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An Examination of Classification Accuracy in the Continuous Testing Framework
Educational Measurement: Issues and Practice ( IF 2.7 ) Pub Date : 2020-10-13 , DOI: 10.1111/emip.12398
Whitney Smiley Coggeshall 1
Affiliation  

The continuous testing framework, where both successful and unsuccessful examinees have to demonstrate continued proficiency at frequent prespecified intervals, is a framework that is used in noncognitive assessment and is gaining in popularity in cognitive assessment. Despite the rigorous advantages of this framework, this paper demonstrates that there is significant inflation in false negatives as both passers and failers continually take a test, especially for examinees closer to the passing score. Several passing policies are investigated to control the inflation of false negatives while maintaining low false‐positive rates for fixed‐length tests. Lastly, recommendations are made for testing professionals who wish to utilize the rigorous nature of the continuous testing framework while also avoiding the inflation of qualified examinees failing.

中文翻译:

连续测试框架中的分类精度检查

连续测试框架是成功和失败的考生都必须在预定的频繁间隔内证明其持续熟练程度的框架,它是一种用于非认知评估的框架,并且在认知评估中越来越受欢迎。尽管此框架具有严格的优势,但本文证明,通过者和失败者都不断接受考试,尤其是对于接近及格分数的考生,假阴性的通货膨胀率很高。为了控制假阴性的膨胀,同时为固定长度测试保持较低的假阳性率,研究了几种合格的政策。最后,为希望利用连续测试框架的严格特性同时又避免合格的考生失败的测试专业人​​员提出了建议。
更新日期:2020-10-13
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