Overcoming programming barriers for non-computing majors in data science

This National Science Foundation (NSF) STEM Education (CFDA 47.076) grant award for $749,861 aims to develop effective curricular materials and an integrated learning platform to overcome programming barriers and increase data science education for non-computing students.

The 3-year project, led by the Rochester Institute of Technology (RIT) in collaboration with Howard University and Bryn Mawr College, will: (1) create an integrated teaching and learning platform; (2) develop course modules covering key data science topics with hands-on assignments tailored to diverse disciplines; (3) deploy and evaluate the platform and modules at the three institutions, including an HBCU and women's college; and (4) study the impact on student learning across computing experience, discipline, gender, and demographics. The project will directly benefit over 1,500 students in 19 courses across 8 majors. Sub-awards to Howard University and Bryn Mawr College support their contributions to curriculum development, implementation, and assessment.

Abstract

This project aims to serve the national interest by strengthening data science education for students in non-computing disciplines in order to meet the national workforce demands for data scientists across many disciplines. The requirement for computing-related prerequisites and programming tasks can be a barrier for non-computing students with other strong data skills to enter the data science workforce. Results from a prior iuse project indicate the effectiveness of increasing student interest and learning outcomes through in-depth hands-on practice with no or little coding involved, supported by a web-based learning platform. This level II iuse:EDU engaged student learning track project is a collaborative effort among rochester institute of technology, howard university, and bryn mawr college that will upgrade the learning platform to provide comprehensive support for both teaching and learning. The project will also develop modules tailored to non-computing students, evaluate the effectiveness of the platform and the curricular materials at the three sites, and facilitate adoption of the project's products at other institutions. The overarching goal of this project is to provide effective curricular materials to overcome the programming barriers, expose students to various data science topics, and teach them how to solve data problems in the context of their own disciplines. The project will: (1) develop an integrated learning platform to support both teaching and learning; (2) develop a set of course modules covering important data science topics with hands-on assignments designed for different disciplines; (3) deploy and evaluate the platform and course modules at three participating institutions including an hbcu and a women?S liberal arts college; and (4) conduct a study to investigate if the impact of the developed platform and course modules on student learning is independent from students' prior computing experience, discipline, gender, and demographics. The project modules can be flexibly integrated into an existing course or be combined together and offered as a regular course, increasing their adaptability to other institutions. This project will directly benefit more than 1500 students enrolled in the targeted 19 courses from more than 8 different majors at the three sites during the project cycle. The study conducted in this project will provide insights on how to offer effective and inclusive data science education. The project will also provide several professional development opportunities (online tutorials, information sessions, regional and conference workshops) and project outcomes and materials will be widely disseminated via multiple channels. The NSF iuse: EDU 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.- subawards are planned for this award.