小学数学三年一度筛查中变化的多重来源

IF 1.9 3区 教育学 Q1 EDUCATION, SPECIAL Learning Disabilities Research & Practice Pub Date : 2022-11-14 DOI:10.1111/ldrp.12296
Garret J. Hall, David Kaplan, Craig A. Albers
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引用次数: 1

摘要

贝叶斯潜在变化得分模型(LCSM)用于比较小学数学计算的三年(秋季、冬季、春季)变化模型和基于概念/应用课程的测量。数据收集自2-5年级的小学生,每个年级约有700至850名学生(47%至54%为女性;78%至79%为白人,10%至11%为黑人,2%至4%为西班牙裔/拉丁裔,2%至40%为亚裔,2%至14%为美洲原住民或太平洋岛民;13%至14%为英语学习者;10%至14%有特殊教育个性化教育计划)。结果与评估规范和先前独立发现的常见非线性增长模式一致。然而,贝叶斯LCSM捕捉到了先前研究中未观察到的实际相关的变化来源。讨论了多层支持系统中筛选和基于数据的决策的实际意义和方法论意义、局限性和未来方向。
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Capturing Multiple Sources of Change on Triannual Math Screeners in Elementary School

Bayesian latent change score modeling (LCSM) was used to compare models of triannual (fall, winter, spring) change on elementary math computation and concepts/applications curriculum-based measures. Data were collected from elementary students in Grades 2–5, approximately 700 to 850 students in each grade (47%–54% female; 78%–79% White, 10%–11% Black, 2%–4% Hispanic/Latino, 2%–4% Asian, 2–4% Native American or Pacific Islander; 13%–14% English learner; 10%–14% had special education individualized education plans). Results converged with common nonlinear growth patterns from the assessment norms and prior independent findings. However, Bayesian LCSMs captured practically relevant sources of change not observed in prior studies. Practical and methodological implications for screening and data-based decision-making in multitiered systems of support, limitations, and future directions are discussed.

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CiteScore
2.60
自引率
11.10%
发文量
21
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