NSF Award
2417365
This project aims to serve the national interest by enabling non-computer science students in all disciplines to develop data literacy and prepare them to incorporate data science techniques in their future careers or graduate education. Given the pervasiveness of data in our world and the fast pace of technological change, increasing numbers of employers and graduate programs across all fields are demanding basic data fluency, literacy, and competency. This project develops a methodology for training faculty in the sciences, humanities, social sciences, and business to develop small data science modules that can be incorporated into their existing classes and are designed to engage students from diverse disciplines, academic backgrounds, and demographics. Faculty will learn how to develop and teach their own modules, enhancing their ability to tailor the content to their specific curriculum and enabling them to create additional modules in the future. The resulting library of modules will be piloted in classes and disseminated broadly to enable faculty nationwide to incorporate data literacy and data science principles across the curriculum. This approach will ultimately result in improved student learning and engagement with data science compared to prior approaches that involve single standalone courses for non-majors instructed by computer science faculty. As faculty across diverse disciplines become aware of the value of data science competencies in their own fields and recognize the benefit for their students, they will contribute to a cultural shift that elevates data science as an integral part of the liberal arts. <br/><br/>The STEM higher education enterprise has yet to identify a single best strategy for teaching data science or data literacy to non-computer science majors. Many previous efforts have focused on a single standalone course for non-majors, but students who are underrepresented in STEM due to feelings of exclusion may also avoid these courses. The project goal is to demonstrate that short data science modules integrated into the existing curriculum across disciplines, as an alternative to a standalone class, will be relevant and engaging for students across all majors. The project will provide workshops and a summer retreat in which faculty instructors (STEM and non-STEM) learn to develop and share data science modules that are of interest to them and their students and appropriate for their disciplines. The professional development activities will be designed to consider a range of faculty backgrounds, the constraints on their time, and the need for them to teach a diverse undergraduate population, including students underrepresented in STEM. In addition, community-building activities, including a career speaker series and data science symposium, are planned to increase awareness in both students and faculty about the importance and relevance of data science to all disciplines. A research study will investigate the value of and challenges in incorporating data science education throughout the curriculum, especially for faculty teaching a diverse student body across multiple majors. The project will also examine how well the module design process and community-building activities help faculty and students understand the importance of data science to their own interests and disciplines. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.<br/><br/>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.
