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，技术和数学专业的学生开发两项后计算后的统计课程，这些课程旨在为智力内容和统计学作为纪律的广泛适用能力提供可靠的介绍，同时尊重参加这些课程的学生的强大定量背景。这两个课程都包含调查实验室模块（LABS），该模块强调了与科学和社会科学专业学生相关的科学和数据分析过程。第一课程中的大部分材料都来自于9950476“统计概念，方法和理论的计算后，积极的学习，积极的学习介绍”（ISCAT）。第二个课程扩展了ISCAT，并利用了格林内尔学院科学课程中目前使用的基于综合研究的实验室方法（部分由DO-9950289资助）。正在开发这两个课程中的实验室，以便可以将它们单独整合到本科统计和其他学科中的许多课程中。该项目的智力优点是通过开发材料来为基于代数的统计学课程和基于询问的科学统计学的统计学课程的成功量化，从而为统计学教育的奖学金提供了良好的素材，以便于早期的统计学课程，以便于早期的统计学课程，以便于早期的学生进行了良好的兴趣。这个合作项目旨在加深本科生的统计知识，他们是未来的科学家和定量社会科学家，以加强未来统计学家与科学研究者之间的跨学科对话。该项目还通过开发一系列强调数据分析并鼓励学生收集数据，确定适当的分析，使用技术，进行分析，进行分析，推断，解释并呈现结果的实验​​室来解决2004年CUPM指南的建议。该项目的更广泛的影响包括创建用于数学，技术和科学专业学生的模型来开发跨学科数据分析和研究技能，通过创建统计数据的第二课程，这些课程很容易适合许多机构的现有课程。这使可能无法在其课程中添加新课程的机构将一些实验室纳入标准概率课程或其他科学课程，从而提高其他学科专业的学生的定量技能。实验室模块的发展汇集了许多学科的信息，并增加了教师在物理，生物学和社会科学方面的合作。通过在专业会议上的演讲，统计教育期刊上的出版物和在线上在CauseWeb（＃DAU-0333672）通过演讲来传播该材料。由ISCAT建模，这些实验室的传播包括通过Internet上的各种数据分析工具（例如Excel，Minitab，Stata，R和Java applets）访问的数据和仿真。