Integrating Content and Skills from the Humanities into Data Science Education

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This project aims to serve the national interest by improving data science education using a human-centered approach to teaching the foundations of data science. The purpose of this project is to understand the impact of efforts dedicated to broaden recruitment and retention in data science. This effort is particularly important for students coming from demographic groups that are currently underrepresented in data science. These future data scientists will need to analyze not just numbers, but their human contexts and consequences, to prevent intentional or unintentional misuse of data science, and to communicate results effectively. This project has been designed to test if these goals might be achieved by integrating content and skills from the humanities into data science education. A team-taught interdisciplinary approach will be used to create and deliver an introductory data science course for undergraduate students. The course will use real-world social issues to teach important statistics and coding skills alongside ways of thinking from the humanities. Examples of such thinking include analysis of the source of data and the harms and benefits of data collection and analysis. It also includes the rhetorical aims and strategies of those who use data in politics and policymaking. The effectiveness of the course in improving student learning will be assessed to determine how this education model can be improved, adapted, and ultimately implemented at other colleges and universities. This project has potential to craft a more inclusive and human-centered approach to teaching the foundations of data science. By developing a new collaborative model of data science education that can be adapted nationwide, this project aims to positively impact STEM education, leading to a more diverse, creative, and innovative national workforce and a more STEM-literate public.This project’s goal is to develop and assess new pedagogical approaches to collaboratively teaching data science that effectively incorporate perspectives of both STEM and humanities disciplines. The resulting introductory data science course will provide future data science and STEM majors with qualitative reasoning skills that are traditionally taught in the humanities, provide future humanities majors with an on-ramp to further study of data science, and provide all students with statistical and computational skills they can apply in future courses and in the workforce. This project will collect evidence to answer four research questions: (1) In what ways and to what degree does the humanities-focused introductory data science course change attitudes about STEM, data science, and the humanities? (2) How effective is the course in helping students achieve student learning outcomes in both data science and the humanities? (3) How effective is the proposed peer assessment system? (4) To what extent does the project increase the recruitment and retention of diverse students in data science, STEM, and the humanities? These questions will be addressed using surveys of students’ attitudes toward STEM and the humanities, assessments of student learning outcomes from an interdisciplinary data science course, the development and evaluation of a peer assessment program, multiple assessments of teaching effectiveness, and a longitudinal study of student pathways and performance. Ultimately, the answers to these questions will provide insights on how to educate more data scientists, improve student learning, and provide non-scientists with the ability to understand and interpret foundational concepts in data science. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.