Interdisciplinary computing applied computing for behavioral and social sciences
SIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education
As the digital economy grows, so does the demand for technology-capable workers who have both computing skills and domain expertise. Growing such a workforce is critical to ensuring the nation's competitiveness, according to a recent National Science Board publication. To address this need, faculty from the Colleges of Engineering and Social Sciences at San Jose State University worked together to create the Applied Computing for Behavioral and Social Sciences minor degree. The minor targets students in majors such as Psychology and Economics, which have a more diverse student population than that of Computer Science or Engineering. The minor, designed with industry input, includes a four-course sequence that focuses on Python and R and includes topics such as data structures, algorithms, data cleaning and management, and data analysis. Our cohort-based program was built specifically for social science students using social science content, helping to foster a sense of community and belongingness among students. The first full cohort of 26 students graduated in Spring 2019, 48% of whom were female and 23% of whom were underrepresented minorities. Our approach of embedding computing education into the social sciences demonstrates a promising model of broadening participation in computing and meeting the nation's increasing demand for interdisciplinary computing workers in the digital age.
National Science Foundation
Applied computing, Broadening participation in computing, Computing for social sciences, Data science for social sciences, Interdisciplinary computing, New degree program
Psychology; Electrical Engineering; General Engineering
Valerie Carr, Morris Jones, and Belle Wei. "Interdisciplinary computing applied computing for behavioral and social sciences" SIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education (2020): 400-406. https://doi.org/10.1145/3328778.3366799