Developmental skills linked to math and science achievement: An interdisciplinary data-intensive approach to identification and improvement through experimental intervention

《与数学和科学成就相关的发展技能：通过实验干预识别和改进的跨学科数据密集型方法》是科学与工程教育研究和评估(REESE)计划的一个为期5年的项目，目的是更好地理解和设计(和测试)干预措施，以解决基础技能方面的缺陷，研究人员假设这些缺陷可能有助于缩小种族/SES以及数学和科学领域的国际差距。这些假设表明，某些早期技能的缺陷是种族/SES数学和科学差距的相当大比例的基础，而具有显著技能差距的很大比例(约一半)的美国学生是国际数学/科学成绩平平的重要部分。这项工作建立在之前NSF资助的研究的基础上。拟议研究的目标是：(1)扩大用于确定和表征与后来的数学/科学有关的发展技能的纵向数据集，(2)加强用于将早期发展技能与后来的成就联系起来的统计方法，并纳入数学/科学分量表水平的分析，(3)引入一种新的统计方法--潜伏期分析--将使用其多种技能分布的儿童分组到具有相似特征的组中，并利用生长模型预测他们在技能变化时的成就轨迹，(4)正规化和缩短干预措施的设计、开发和实验测试所涉及的过程，(5)为从学前班到低年级的一群儿童制定综合干预措施和使用多种干预措施的战略，以消除成绩差距，以及(6)制定一项全面的国家战略和优先事项，以解决与数学/科学成绩差距有关的技能缺陷。

"Developmental skills linked to math and science achievement: An interdisciplinary data-intensive approach to identification and improvement through experimental intervention" is a 5-year project of the Research and Evaluation on Education in Science and Engineering (REESE) program to better understand and devise (and test) interventions to address deficits in foundational skills that the investigators hypothesize may help to close racial/SES and international gaps in math and science. The hypotheses suggest that deficits in certain early skills underlie a significant proportion of racial/SES math and science gaps, and the large proportion (about one-half) of U.S. students with significant skill gaps underlies a significant part of mediocre international math/science scores. This work builds on prior NSF-supported research. Goals of the proposed research are to: (1) expand longitudinal data sets used to identify and characterize the developmental skills linked to later math/science, (2) strengthen the statistical methodology used to link early developmental skills to later achievement and incorporate analysis at the math/science subscale level, (3) introduce a new statistical methodology - latent profile analysis - to cluster children using their multiple skill distributions into groups with similar profiles and utilize growth models to predict their achievement trajectories as skills change, (4) formalize and shorten the process involved in the design, development and experimental testing of interventions, (5) develop strategies for combining interventions and using multiple interventions for a cohort of children from preschool to early grades that would eliminate achievement gaps, and (6) formulate a comprehensive national strategy and priorities for addressing the skill deficits linked to math/science achievement gaps.