Developing an Innovative Randomization-based Introductory Statistics Curriculum

开发基于随机化的创新统计入门课程

• 批准号: 1140629
• 负责人: Nathan Tintle
• 金额: $ 18.15万
• 依托单位: Dordt University, Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1140629&HistoricalAwards=false
• 关键词: Developing; Innovative; Randomization; based; Introductory

The algebra-based introductory statistics course has seen tremendous growth in enrollments over the last two decades using a consensus curriculum and sequencing of topics. However, research has also shown students typically leave these courses with a shallow understanding of key inferential ideas. Recently, many statistics educators have proposed moving from this traditional curriculum to one centered on computer-intensive, randomization-based inference methods. Two advantages of this approach are: (1) randomization methods enable students to focus on the core logic of inference, and (2) efficiency in presentation allows students to gain experience in computer-intensive and multivariable methods that are being increasingly used by applied researchers. This project is providing instructors with a fully integrated set of curriculum materials with which to teach a substantially different curriculum that introduces statistical inference from the start. The materials are undergoing class-testing at numerous institutions and being disseminated through publication as a textbook, workshops, and presentations. The accompanying evaluation component is providing information about potential gains in student understanding of core concepts of inference and documentation of how students develop skills of inferential reasoning. These curricular materials and assessment findings have the potential for effecting a substantial change in the content and focus of introductory statistics courses across the country.

在过去的二十年中，基于代数的统计学入门课程使用共识课程和主题顺序，入学率大幅增长。然而，研究也表明，学生离开这些课程时，通常对关键的推理思想理解肤浅。最近，许多统计学教育工作者建议从这种传统课程转向以计算机密集型、基于随机化的推理方法为中心的课程。这种方法的两个优点是：(1)随机化方法使学生能够专注于推理的核心逻辑；(2)演示的效率使学生能够获得越来越多应用研究人员使用的计算机密集型和多变量方法的经验。该项目为教师提供了一套完整的课程材料，用于教授一门从一开始就介绍统计推断的完全不同的课程。这些材料正在许多机构进行班级测试，并通过出版教科书、讲习班和演讲等方式进行传播。随附的评估部分提供了关于学生理解推理核心概念的潜在收益的信息，并记录了学生如何培养推理推理技能。这些课程材料和评估结果有可能对全国统计入门课程的内容和重点产生重大变化。