Data Path: Creating a New STEM Pathway for Undergraduates from Statistics Into Data Science

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This project aims to serve the national interest by increasing the number and diversity of STEM students. To do so, it will establish a new path into STEM majors and careers through statistics and data science for students at a Hispanic-serving two-year college. This new path is expected to improve STEM learning and teaching and increase the diversity of students pursuing STEM. The first step in the pathway will be a redesigned Introduction to Statistics course that incorporates project-based learning. This change is expected to engage more students and spark their interest in pursuing a STEM pathway. The next step in the pathway will be a new introduction to Data Science course, which will meet a transfer math requirement The Data Science course is expected to serve as an onramp into STEM for students who do not necessarily see themselves in a STEM career. The project will actively recruit students from the new statistics course into the new Data Science course, thus transforming the statistics course into a STEM talent pipeline instead of a terminal math course. Additional students will be recruited into the Data Science course via a new Data Scholars Program, which will provide a supportive STEM community for students who may not be initially interested in STEM majors. Finally, the project will provide professional development to mathematics faculty on implementing project-based curriculum pedagogy. A quasi-experimental design will be used to measure the overall effectiveness of project-based learning in the introductory statistics course. Students in the new project-based learning statistics course will be compared with students in statistics courses using traditional pedagogy. Analysis of variance will be used to examine differences between the two types of courses on measures of student experience, student attendance, course success, degree completion, and transfer rate to four-year universities. Furthermore, qualitative methods, such as focus groups and open-ended survey questions, will be used to evaluate the fidelity of implementing the project-based learning model. This project project may serve as a model for other community colleges looking to build a data science program and a new pathway into STEM. Lessons learned and insights gained into how changing pedagogy impacts student interest and success in STEM will be shared through conference presentations and publications. Anticipated outcomes during the project period include increased student success rates in the project-based statistics course, increased enrollment in the data science course, and increased participation in the Data Scholars program. 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.