A Technology Pathway Program in Data Technology and Applications

Valerie A. Carr; Morris E. Jones; Belle Wei

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Conference: 2020 ASEE Virtual Annual Conference Content Access
Location: Virtual On line
Publication Date: June 22, 2020
Start Date: June 22, 2020
End Date: June 26, 2021
Conference Session: NSF Grantees: Workforce Development
Tagged Topics: Diversity and NSF Grantees Poster Session
Page Count: 14
DOI: 10.18260/1-2--34076
Permanent URL: https://peer.asee.org/34076
Download Count: 419

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Paper Authors

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Valerie A. Carr San Jose State University

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Valerie Carr earned a PhD in Neuroscience from UCLA and completed a postdoctoral fellowship in Cognitive Neuroscience at Stanford University. She is now an Assistant Professor in the Department of Psychology at San Jose State University where she conducts research regarding learning and memory across the lifespan and teaches courses relating to memory and neuroscience. She actively collaborates with faculty across several departments on SJSU’s minor in Applied Computing for Behavioral and Social Sciences (ACBSS), and she teaches the first course in the minor series. This course covers the application of Python to current social science topics, as well as the use of programming in careers such as data analysis, user experience research, and econometrics.

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Morris E. Jones Jr. San Jose State University

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Morris is retired from the semiconductor industry, and teaches Electrical Engineering, and General Engineering classes at San Jose State University.

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Belle Wei San Jose State University

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Belle Wei is Carolyn Guidry Chair in Engineering Education and Innovative Learning at San José State University (SJSU). Previous roles include: Provost and Vice President for Academic Affairs at California State University, Chico; a decade of service as the Don Beall Dean of Engineering in the Charles W. Davidson College of Engineering at SJSU; faculty member of SJSU since 1987; and visiting Associate Professor at Stanford University in 1993. She is founder and Board Chair of the Center for Advancing Women in Technology, which established the Technology Pathways Initiative (TPI) in 2015. The TPI provides computing education to more diverse students by developing new interdisciplinary computing programs through an alliance of universities and industry. In 2006, Dr. Wei spoke before the U.S. Congress about the “Innovation Agenda,” contributing to the America COMPETES Act (2007). Dr. Wei holds a Ph.D. in Electrical Engineering and Computer Sciences and a B.S. in Biophysics from the University of California at Berkeley, and an M.S. in Engineering from Harvard University.

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Abstract

With an exponential increase in user-generated data, there is a strong and increasing demand for employees possessing both technical skills and knowledge of human behavior. Supported by funds from the National Science Foundation Division of Undergraduate Education, we have begun to address this need by developing a technology pathway program in data technology and applications at a large, minority-serving public university. As part of this program, an interdisciplinary team of faculty created a new minor in Applied Computing for Behavioral and Social Sciences. A large number of diverse students are studying behavioral and social sciences, and the ability to model human behaviors and social interactions is a highly valuable skill set in our increasingly data-driven world. Applied Computing students complete a four-course sequence that focuses on data analytics and includes data structures and algorithms, data cleaning and management, SQL, and a culminating project. Our first full cohort of students completed the Applied Computing minor in Spring 2019. To assess the success of the minor, we conduct student surveys and interviews in each course. Here, we focus on survey data from the beginning and end of the first course, given that it served as a particularly important feedback loop to optimize the course and to inform the design and execution of subsequent courses. The data reflect a significant increase in confidence in programming abilities over time, as well as a shift in attitudes about programming that more closely matches those of experts. The data did not show a significant change in mindset over time, such that students maintained a growth mindset across the semester. Finally, with respect to goals, students placed a greater emphasis on data and tech at the end of the semester, highlighting specific career paths such as user experience and human factors. In the future, we plan to administer this same survey to social science students not involved in the minor to serve as a control group and to begin exploring the large dataset obtained from other courses in the minor. We believe that embedding computing education into the social sciences is a promising means of diversifying the technical workforce and filling the need for interdisciplinary computing professionals, as evidenced by high rates of female and underrepresented minority enrollment in our courses, as well as promising shifts in student confidence, attitudes, and career goals as a result of taking Applied Computing courses.

Citation
Format

Carr, V. A., & Jones, M. E., & Wei, B. (2020, June), A Technology Pathway Program in Data Technology and Applications Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34076

TY  - CPAPER
AB  - With an exponential increase in user-generated data, there is a strong and increasing demand for employees possessing both technical skills and knowledge of human behavior. Supported by funds from the National Science Foundation Division of Undergraduate Education, we have begun to address this need by developing a technology pathway program in data technology and applications at a large, minority-serving public university. As part of this program, an interdisciplinary team of faculty created a new minor in Applied Computing for Behavioral and Social Sciences. A large number of diverse students are studying behavioral and social sciences, and the ability to model human behaviors and social interactions is a highly valuable skill set in our increasingly data-driven world. Applied Computing students complete a four-course sequence that focuses on data analytics and includes data structures and algorithms, data cleaning and management, SQL, and a culminating project. Our first full cohort of students completed the Applied Computing minor in Spring 2019. To assess the success of the minor, we conduct student surveys and interviews in each course. Here, we focus on survey data from the beginning and end of the first course, given that it served as a particularly important feedback loop to optimize the course and to inform the design and execution of subsequent courses. The data reflect a significant increase in confidence in programming abilities over time, as well as a shift in attitudes about programming that more closely matches those of experts. The data did not show a significant change in mindset over time, such that students maintained a growth mindset across the semester. Finally, with respect to goals, students placed a greater emphasis on data and tech at the end of the semester, highlighting specific career paths such as user experience and human factors. In the future, we plan to administer this same survey to social science students not involved in the minor to serve as a control group and to begin exploring the large dataset obtained from other courses in the minor. We believe that embedding computing education into the social sciences is a promising means of diversifying the technical workforce and filling the need for interdisciplinary computing professionals, as evidenced by high rates of female and underrepresented minority enrollment in our courses, as well as promising shifts in student confidence, attitudes, and career goals as a result of taking Applied Computing courses.
AU  - Valerie A. Carr
AU  - Morris E. Jones Jr.
AU  - Belle Wei
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - A Technology Pathway Program in Data Technology and Applications
UR  - https://peer.asee.org/34076
DO  - 10.18260/1-2--34076
ER  -