Author(s):
Need: This project entitled “Data science for Undergraduates to Broaden Opportunities for advancing an Inclusive Society (DUBOIS)” is a diverse collaborative structure consisting of a research-intensive institution and two minority serving institutions located at distal sites in California and Alabama: UC Berkeley, UC Merced and Tuskegee University. The focus of the DUBOIS Alliance is to implement a novel, stand-alone and interdisciplinary Data Science curriculum on each campus as a shared project goal with a view to broaden participation in the Data Science sector through all its activities. The effort is motivated by a pressing need to provide Data Science experiences to talent from communities that have traditionally been excluded from this sector. A distinctive characteristic of the Data Science curriculum that is being developed is its cross-cutting feature, residing at the convergence of Data, Computer Science (CS) and the Social and Behavioral Sciences (BSS) wherein students will collaborate to investigate Data Science problems in the context of BSS applications. The resulting 14-week course will empower students, with or without background in CS, with informed, computing solutions to societal challenges. Guiding Questions: The DUBOIS research questions are designed to study student experiences in the Data Science course and the hybrid classroom design proposed during partnership course delivery:RQ-1: What aspects of the course curriculum build learner confidence in computational thinking and data science, particularly among minoritized students?RQ-2: What aspects of the hybrid instruction model engage students with the course material, particularly around culturally responsive computing?RQ-3: What aspects of the hybrid instruction model build instructor confidence and competency?Outcomes: The DUBOIS Alliance aims to broaden participation in Data Science by providing Undergraduate students at three distal institutions situated in unique cultural settings with stimulating learning experiences through a novel curriculum. The strategies that will be employed in the future Data Science classrooms at the three sites are expected to promote the formation of Data Science identities amongst students from diverse communities, seeing themselves as Data Scientists and, in turn, building interest in pursuing careers in computationally rich STEM areas where Data Analytics plays a central role. Broader Impacts: The DUBOIS Alliance activities will establish an introductory Data Science curriculum at the convergence of Data, Computing and Behavioral Sciences. This novel approach with broad impact potential is expected to upskill instructors in teaching an interdisciplinary, socially engaged course taught in different cultural settings. Moreover, on the students’ side, the Alliance’s strategies are expected to promote deep learning of Data Science concepts in highly collaborative, inclusive, multidisciplinary settings, where they will have opportunities to explore data from contexts relevant to their lived experiences and make inferences through peer-led discussions. The Data Science curriculum is being designed for scale for use by any post-secondary institution across the country and be teachable in a variety of higher education contexts, from large public research institutions to minority-serving institutions, following in-class testing within the DUBOIS Alliance and refinements through evaluation feedback.
Coauthors
Lisa Yan, UC Berkeley, Berkeley, CA; Suzanne Sindi, UC Merced, Merced, CA; Yasmeen Rawajfih, Tuskegee University, Tuskegee, AL; Dave Harding, UC Berkeley, Berkeley, CA; Deb Nolan, UC Berkeley, Berkeley, CA; Juan Meza, UC Merced, Merced, CA; Roummel Marcia, UC Merced, Merced, CA; Rosemarie Bongers, UC Merced, Merced, CA; Vivian Carter, Tuskegee University, Tuskegee, AL; Fan Wu, Tuskegee University, Tuskegee, AL; Mandoye Ndoye, Tuskegee University, Tuskegee, AL; Mohammed Qazi, Tuskegee University, Tuskegee, AL; Johnnie Williams, Laney College, Oakland, CA; Kyla Oh, Laney College, Oakland, CA; Erica Rutter, UC Merced, Merced, CA; Heather Bortfeld, UC Merced, Merced, CA; Alex Strang, UC Berkeley, Berkeley, CA
