Collaborative Research: Integrating Science and Active Learning into Data-Oriented Post-Calculus Probability and Statistics Courses

协作研究：将科学和主动学习整合到面向数据的微积分后概率与统计课程中

• 批准号: 0510392
• 负责人: Shonda Kuiper
• 金额: $ 9.74万
• 依托单位: Grinnell College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0510392&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; Science; Active

Mathematical Sciences (21)The project is developing two post-calculus statistics courses for science, technology, and mathematics students that are designed to provide a solid introduction to the intellectual content and broad applicability of statistics as a discipline while respecting the strong quantitative backgrounds of the students who take these courses. Both courses contain investigative laboratory modules (labs) that emphasize the process of science and data analysis relevant for science and social science students. Much of the material in the 1st course is being adapted from DUE-9950476 "A Data-Oriented, Active Learning, Post-Calculus Introduction to Statistical Concepts, Methods, and Theory" (ISCAT). The 2nd course extends ISCAT and utilizes integrative research-based lab methodology currently used in science courses at Grinnell College (partially funded by DUE-9950289). Labs in both courses are being developed so that they can be individually integrated into many courses in both undergraduate statistics and other disciplines.The intellectual merit of this project is to contribute to the scholarship of statistics education by developing material that expands the successful reforms of the algebra-based introductory statistics course and inquiry-based science courses into early statistics courses designed for students with strong quantitative skills and interests. This collaborative project aims to deepen the statistical knowledge of undergraduates who are future scientists and quantitative social scientists in order to strengthen the interdisciplinary dialog between statisticians and scientific investigators in the future. The project also addresses a 2004 CUPM Guide recommendation by developing a series of labs that emphasize data analysis and encourage students to collect data, determine an appropriate technique for analysis, use technology, perform the analysis, make inference, interpret and then present the results. The broader impacts of the project include creation of models for mathematics, technology and science students to develop interdisciplinary data analysis and research skills by creating a 2nd course in statistics that easily fits into existing curricula at many institutions. This allows institutions that may not be able to add new courses to their curriculum to incorporate a few labs into a standard probability course or other science courses, thereby increasing the quantitative skills of students majoring in other disciplines. The development of the laboratory modules brings together information across many disciplines and increases collaboration between faculty in the physical, biological and social sciences. This material is being disseminated through presentations at professional meetings, by publication in statistics education journals, and online at CAUSEweb (#DUE-0333672). As modeled by ISCAT, dissemination of these labs includes data and simulations that are accessible through a variety of data analysis tools such as Excel, Minitab, Stata, R, and java applets on the internet.

数学科学（21）该项目正在为科学，技术和数学学生开发两门后微积分统计课程，旨在为统计学作为一门学科的知识内容和广泛适用性提供坚实的介绍，同时尊重参加这些课程的学生的强大定量背景。这两门课程都包含调查实验室模块（实验室），强调科学和数据分析相关的科学和社会科学的学生的过程。第一门课程中的大部分材料都是改编自DUE-9950476“面向数据，主动学习，后微积分统计概念，方法和理论介绍”（ISCAT）。第二门课程扩展了ISCAT，并利用了格林内尔学院目前在科学课程中使用的综合研究实验室方法（部分由DUE-9950289资助）。这两门课程的实验室正在开发中，以便它们可以单独整合到本科统计学和其他学科的许多课程中。该项目的智力价值是通过开发材料，扩大基于代数的入门统计学课程和调查的成功改革，为统计学教育的奖学金做出贡献。为具有较强的定量技能和兴趣的学生设计的早期统计课程。该合作项目旨在加深未来科学家和定量社会科学家的本科生的统计知识，以加强统计学家和科学调查人员之间的跨学科对话。该项目还通过开发一系列强调数据分析的实验室，鼓励学生收集数据，确定适当的分析技术，使用技术，执行分析，进行推理，解释并展示结果，从而解决了2004年CUPM指南的建议。该项目的更广泛影响包括为数学、技术和科学学生创建模型，通过创建第二门统计课程，培养跨学科的数据分析和研究技能，该课程很容易融入许多机构的现有课程。这使得那些可能无法在课程中增加新课程的机构能够将一些实验室纳入标准概率课程或其他科学课程，从而提高其他学科学生的定量技能。实验室模块的开发汇集了许多学科的信息，并增加了物理，生物和社会科学教师之间的合作。这份材料正在通过在专业会议上的介绍、在统计教育期刊上发表以及在CAUSEweb（#DUE-0333672）上在线传播。正如ISCAT所模拟的那样，这些实验室的传播包括通过各种数据分析工具（如Excel、Minitab、Stata、R和互联网上的Java小程序）访问的数据和模拟。