揭开适当增长百分位数的神秘面纱

IF 2.7 4区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Katherine E. Castellano, Daniel F. McCaffrey, Joseph A. Martineau
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引用次数: 0

摘要

成长到标准模型评估学生的成长与达到未来标准或兴趣目标(如熟练程度)所需的成长。一个常见的增长到标准模型涉及比较流行的学生增长百分位数(SGP)和适当增长百分位数(AGPs)。agp是基于一系列非线性分位数回归模型拟合纵向学生考试成绩数据的一个复杂过程。本文通过在更熟悉的线性回归框架中推导agp来揭开它们的神秘面纱。这进一步表明,与sgp不同,agp和基于agp的轨道分类与地位密切相关。最后,对agp的分类精度进行了评价。一项实证研究和分析推导表明,agp可能是学生未来表现的有问题的指标,以前不熟练的学生更有可能被错误地标记为没有走上正轨,而以前熟练的学生则被错误地标记为走上正轨。这些分类错误在个人和学校层面都有公平的含义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demystifying Adequate Growth Percentiles

Growth-to-standard models evaluate student growth against the growth needed to reach a future standard or target of interest, such as proficiency. A common growth-to-standard model involves comparing the popular Student Growth Percentile (SGP) to Adequate Growth Percentiles (AGPs). AGPs follow from an involved process based on fitting a series of nonlinear quantile regression models to longitudinal student test score data. This paper demystifies AGPs by deriving them in the more familiar linear regression framework. It further shows that unlike SGPs, AGPs and on-track classifications based on AGPs are strongly related to status. Lastly, AGPs are evaluated in terms of their classification accuracy. An empirical study and analytic derivations reveal AGPs can be problematic indicators of students’ future performance with previously not proficient students being more likely incorrectly flagged as not on-track and previously proficient students as on track. These classification errors have equity implications at the individual and school levels.

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来源期刊
CiteScore
3.90
自引率
15.00%
发文量
47
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