REESE Empirical Research on Emerging Topics in STEM Education: Statistical Methods for Assessing Teaching and Program Effectiveness

REESE STEM 教育新兴主题的实证研究：评估教学和项目有效性的统计方法

• 批准号: 0909630
• 负责人: Sharon Lohr
• 金额: $ 30.89万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0909630&HistoricalAwards=false
• 关键词: REESE; Empirical; Research; Emerging; Topics

This basic research project will develop new statistical techniques that will provide more robust estimates of the Value-Added Models (VAM). Multivariate response value-added models will be developed to include continuous and categorical responses and nested data structures, and address missing data problems. These models will employ latent-class mixture models, and will use classification trees and random forest methods for data analyses. The new techniques will allow the models to be used not only with continuous response data, such as test scores, but also categorical response data such as completion of a STEM degree. The techniques will also allow researchers to investigate the effects of missing data on value added models, as can occur when students drop out of STEM degree programs during college. The models will improve upon the current VAM models in three aspects: 1) incorporating the various missing data structures, 2) considering both continuous and categorical outcomes, and 3) taking into account complex relationships among subgroups of students and program characteristics. The potential benefits of developing such value added statistical models will be for informing educational policy and practice. These benefits will include better decisions based on more precise estimates of teacher effects and the effects of other inputs on student outcomes in STEM. The researchers propose to address limitations of current value-added models to provide stronger models for assessing STEM program effectiveness and measure teacher or school effects on student achievement.

这个基础研究项目将开发新的统计技术，为增值模型（VAM）提供更可靠的估计。将开发多变量响应增值模型，以包括连续和分类响应以及嵌套数据结构，并解决缺失数据问题。这些模型将采用潜在类别混合模型，并将使用分类树和随机森林方法进行数据分析。新技术将允许模型不仅用于连续响应数据，如考试成绩，还可以用于分类响应数据，如完成STEM学位。这些技术还将使研究人员能够调查缺失数据对增值模型的影响，就像学生在大学期间辍学时可能发生的那样。该模型将在三个方面改进当前的VAM模型：1）合并各种缺失的数据结构，2）考虑连续和分类结果，3）考虑学生和项目特征之间的复杂关系。 开发这种增值统计模型的潜在好处是为教育政策和实践提供信息。这些好处将包括基于对教师影响的更精确估计以及其他投入对STEM学生成果的影响做出更好的决策。研究人员建议解决当前增值模型的局限性，为评估STEM项目的有效性提供更强大的模型，并衡量教师或学校对学生成绩的影响。