Infusing Data Science into Undergraduate STEM Education

在加速发现计划的支持下，该项目将开发在本科STEM课程中注入数据科学教学的方法和材料。数据科学的注入将从最早的STEM微积分先决课程开始开始，并通过顶点体验继续。 目标课程包括科学，数学和工程方面的现有STEM课程。通过STEM课程的进展将被开发，为学生提供数据科学能力，包括获取，管理，分析，可视化和从数据中得出结论。将开发的项目材料包括顺序模块，课程和课外项目和实习，以及跨学科团队的顶点项目。该项目还将创建一个“数据科学中心”，以促进学生团队的形成和指导，以积极参与在物理和自然科学，数学和工程中应用数据科学方法。由于数据科学的专业知识正在成为任何STEM领域工作的必要组成部分，该项目旨在将数据科学融入STEM学科，以加强本科生的学习和为STEM劳动力做准备。该项目将采用综合方法，其中数据科学作为数学，科学和工程的跨学科连接器。 该项目旨在对由此产生的大学一级综合教学的结果进行全面的研究。具体而言，该项目旨在生成有关课程干预是否以及如何有效地使学生接触数据科学的证据，并确定这种接触是否激励学生获得数据科学的进一步培训并追求数据科学职业。学生学习成果数据将为本科STEM教育中注入数据科学的最佳实践提供信息。该项目将为STEM领域的教师举办一个研讨会，以扩大共享该项目中开发的课程材料的影响。此外，用于注入数据科学的模块和课程材料将作为网络资源免费提供。通过将数据科学注入STEM学科，该项目旨在提高本科生对数据科学的接触和专业知识，并加强他们进入STEM劳动力的准备。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Accelerating Discovery program, this project will develop methods and materials for infusing data science instruction within the undergraduate STEM curriculum. Infusion of data science will begin with the earliest STEM calculus prerequisite courses and continue through capstone experiences. Targeted courses include existing STEM classes in science, mathematics, and engineering. Progressions through STEM curricula will be developed to provide students with data science competencies including acquiring, managing, analyzing, visualizing, and drawing conclusions from data. The project materials that will be developed include sequenced modules, curricular and co-curricular projects and internships, and capstone projects for interdisciplinary teams. The project will also create a "data science hub" to facilitate the forming and mentoring of student teams to actively engage in applying data science methods within the physical and natural sciences, mathematics, and engineering.As expertise in data science is becoming a required component for work in any STEM field, this project aims to integrate data science into the STEM disciplines to enhance undergraduate student learning and preparation for the STEM workforce. The project will use an integrative approach in which data science serves as the interdisciplinary connector of mathematics, science, and engineering. The project aims to conduct a comprehensive research study of the outcomes of the resulting integrative instruction at the collegiate level. Specifically, the project seeks to generate evidence about whether and how the curricular interventions effectively expose students to data science and to determine if this exposure motivates students to attain further training in data science and to pursue data science careers. Student learning outcomes data will inform best practices for infusing data science in undergraduate STEM education. The project will host a workshop for faculty in STEM fields to expand the impact of sharing the curricular materials developed in the project. Additionally, modules and curricular materials for infusing data science will be available freely as web resources. By infusing data science in the STEM disciplines, the project seeks to enhance undergraduate students' exposure to and expertise in data science as well as enhance their preparation to enter the STEM workforce.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.