Collaborative Research: An Agent-Based Simulation Environment for Predictive Longitudinal Modeling of High School Math Performance

协作研究：基于代理的模拟环境，用于高中数学成绩的预测纵向建模

• 批准号: 1119332
• 负责人: Michael Strong
• 金额: $ 38.26万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-11-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1119332&HistoricalAwards=false
• 关键词: Collaborative; Research; Agent; Based; Simulation

This collaborative proof-of-concept study involves The University of California at Santa Cruz, The University of Texas at Austin, and the Los Alamos National Laboratory. The PIs will test the efficacy of using agent-based simulation and visualization models to identify the factors that predict mathematics achievement for students from the 8th grade to the 12th grade and beyond. The team are using a data set that includes 14 years of data on student grade reports, coursework, demographics, teacher variables such as years of service, professional development courses take, years of service, and other artifacts. The investigators hypothesize that agent-based modeling can be used to improve mathematics education. The research questions is What are the predictors of success in mathematics of public school 8th grade students and beyond as measured by a) mathematics performance (test scores) broken down by different mathematical skills? b) enrollment in algebra class (8th grade and high school)? and c) algebra and mathematics grades in 8th grade and high school? This exploratory study will analyze data using three tasks. The first task involves data assessment. The first task will involve discovering distributional information in general. They will explore visual and analytical processes of different variables so that different synthetic data can be simulated. The second task involves collaborating with a statistical science team to incorporate distributional information so that multivariate samples can be generated to form synthetic populations to use to build the agent-based model. The third task involves using the actual data from two large school districts to understand and quantify variability in the data. Education systems do not have a valid way to measure progressions of mathematics education to evaluate outcomes associated with mathematics learning outcomes. This project will provide a baseline understanding of student's progression in mathematics achievement that is critical in helping educators and policy makers set goals and standards for mathematics education within the United States.

这项概念验证的合作研究包括加州大学圣克鲁斯分校、德克萨斯大学奥斯汀分校和洛斯阿拉莫斯国家实验室。pi将测试使用基于主体的模拟和可视化模型的有效性，以确定预测8年级至12年级及以上学生数学成绩的因素。该团队使用的数据集包括14年的学生成绩报告、课程作业、人口统计数据、教师变量（如服务年限、专业发展课程、服务年限和其他工件）的数据。研究者假设基于主体的建模可以用来改善数学教育。研究的问题是什么是公立学校八年级及以上学生数学成功的预测因素？ a)数学表现（考试分数）按不同的数学技能进行划分？B)参加代数课程（八年级和高中）？c)八年级和高中的代数和数学成绩？这个探索性研究将使用三个任务来分析数据。第一项任务涉及数据评估。第一个任务将涉及发现一般的分布信息。他们将探索不同变量的可视化和分析过程，以便模拟不同的合成数据。第二个任务涉及与统计科学团队合作，整合分布信息，以便生成多变量样本，形成用于构建基于代理的模型的合成总体。第三项任务涉及使用来自两个大学区的实际数据来理解和量化数据中的可变性。教育系统没有一个有效的方法来衡量数学教育的进展，以评估与数学学习成果相关的结果。该项目将提供学生数学成绩进展的基本了解，这对于帮助教育者和政策制定者制定美国数学教育的目标和标准至关重要。