IUSE Collaborative Research: Data Computing for All: Developing an Introductory Data Science Course in Flipped Format

IUSE 协作研究：全民数据计算：以翻转格式开发数据科学入门课程

• 批准号: 1432438
• 负责人: Christo Dichev
• 金额: $ 13.99万
• 依托单位: Winston-Salem State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1432438&HistoricalAwards=false
• 关键词: IUSE; Collaborative; Research; Data; Computing

This project, Data Computing for All: Developing an Introductory Data Science Course in Flipped Format, addresses two current issues: the need for more students to be familiar with the use of data in a variety of contexts and the need for instructional materials to support coursework in the "flipped classroom" format. The flipped classroom reverses the way time is spent by students. Instead of having information given to them in the classroom and then going away to practice using the new knowledge by themselves through homework problems, the students get the information before class time and then practice using it while the course instructor is present to help with misunderstandings and complexities. This allows the instructor to address the problems encountered by small groups of students, rather than lecturing to a large group without knowing who understands what. Data science is essential for applications as diverse as genome sequencing and customer data analysis. Nearly every discipline has need of large scale data handling that requires computing and statistics in combination. More specifically, this course addresses an important need not only in computer and statistical sciences, but in all science, business, medical and engineering areas in general. The reliance on the effective use of vast amounts of data and the emergent field of data science is changing the operation of organizations across sectors. This project will not only provide new course in an important area in the STEM disciplines, but will also result in valuable educational research.With the growing use of the flipped classroom model of learning, questions arise about its generalizability. Is it always the right approach? How difficult is it to prepare materials to teach a course in this way? This project will address these questions in the context of a new data science course for students with minimal background. The project is being developed in computer science and statistics departments at two different types of institutions. Both institutions have experience using the flipped approach and together have the expertise for data science. The course materials as well as the evaluation results will better enable other institutions to consider including introductory data science computing in their programs.

该项目，所有人的数据计算：以翻转格式开发介绍性数据科学课程，解决了两个问题：需要更多的学生熟悉在各种情况下熟悉数据的使用，以及需要教学材料以支持“翻转教室”格式的课程。 翻转的教室逆转了学生花费时间的方式。 与其在课堂上提供信息，然后通过家庭作业问题自己使用新知识练习，而是在上课时间之前获取信息，然后在课程讲师出席时使用它来帮助解决误解和复杂性。 这使讲师能够解决一小部分学生遇到的问题，而不是在不知道谁了解什么的情况下向大群人讲授。 数据科学对于像基因组测序和客户数据分析一样多样化至关重要。 几乎每个学科都需要大规模数据处理，需要组合计算和统计数据。更具体地说，本课程不仅要在计算机和统计科学中解决重要的需求，而且还解决了所有科学，商业，医学和工程领域的重要需求。依赖有效使用大量数据和新兴数据科学领域的依赖正在改变跨部门组织的运作。该项目不仅将在STEM学科中的重要领域提供新课程，而且还将导致有价值的教育研究。随着翻转的学习模型的日益增长的使用，关于其普遍性的问题。 它总是正确的方法吗？ 准备材料以这种方式教课程有多困难？ 该项目将在新的数据科学课程的背景下为具有最小背景的学生解决这些问题。 该项目正在两种不同类型的机构的计算机科学和统计部门开发。 这两个机构都有使用翻转方法的经验，并共同拥有数据科学的专业知识。 课程材料以及评估结果将更好地使其他机构可以考虑在计划中包括入门数据科学计算。