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

• 批准号: 1252463
• 负责人: David Grissmer
• 金额: $ 249.92万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1252463&HistoricalAwards=false
• 关键词: Developmental; skills; linked; math; science

"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.

“与数学和科学成就相关的发展技能：通过实验干预进行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与评估计划（REESE）的一个为期5年的项目，旨在更好地了解和设计（和测试）干预措施，以解决研究人员假设可能有助于关闭种族/性别歧视的基础技能缺陷。社会经济地位与数学和科学的国际差距。这些假设表明，某些早期技能的不足是种族/SES数学和科学差距的重要原因，而大部分（约一半）具有显著技能差距的美国学生是国际数学/科学成绩平庸的重要原因。这项工作建立在以前NSF支持的研究基础上。拟议研究的目标是：（1）扩大用于识别和表征与后来的数学/科学相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，并在数学/科学子量表水平上纳入分析，（3）引入一种新的统计方法----潜在特征分析法，利用儿童的多种技能分布将他们分成具有相似特征的组，并利用成长模型来预测他们的成就轨迹随着技能的变化，（4）正规化并缩短干预措施的设计、制定和实验测试过程，（5）制定战略，将干预措施结合起来，并对从学前班到低年级的一批儿童使用多种干预措施，以消除成绩差距，以及（6）制定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不足问题。