Release computer science from its silo
![](https://about.jstor.org/wp-content/uploads/2024/07/Noel-Biosimilars-450x434.jpg)
Odra Noel. Biosimilars. n.d. Wellcome Collection.
Plenty of colleges would revel in meteoric growth.
But at computer science departments across the country, explosive demand means big challenges. Thanks to a faculty shortage, class sizes are soaring alongside enrollment. Some schools are throttling the number of computer science majors to manage the gusher — a dispiriting trend for applicants awaiting admissions decisions this spring.
Statistics illustrate the boom: In the 2020-21 academic year, U.S. institutions awarded about 105,000 degrees in computer sciences and engineering, more than double the 43,000 granted a decade earlier. These trends will only intensify with advances in artificial intelligence and the accompanying need to understand and work with the technology.
For such a critical field, colleges and universities need to open more pathways rather than limit seats. One solution: Thread computer science through a variety of disciplines to introduce the subject across more specialties and provide faculty with the training and tools needed to do so. The move wouldn’t just ease pressure on computer science programs but also enrich overall academic experiences and career opportunities for graduates through broader computer and data literacy.
Data cleaning and analysis, especially with Python or R, are valuable skills needed in today’s workforce, regardless of whether students major in humanities, social sciences, or STEM fields. A facility with code and data empowers students and employees to ask questions, gather and assess information, and put findings to practical use. According to the 2023 World Economic Forum Future of Jobs report, employers reported technology literacy to be the third-fastest growing core skill, with AI and big data management not far behind.
More widely, incorporating computer science across curricula would lower barriers to entry. Students who didn’t learn the subject in high school could get a taste without the commitment of a major program. Those who develop an interest could become proficient without switching majors. Even schools without a complete major in the field could help students build their skill sets.
It may also address labor shortages in higher education. Not many schools can match the rich income potential of computer science jobs in the private sector, after all. Industry employment of new computer science doctoral graduates in 2022 far outpaced employment in academia, even as the number of declared undergraduate majors in computer science continued to grow. Of 812 available academic appointments in 2021-22, 63 went unfilled, primarily because candidates declined departments’ offers. Even programs that can hire will need years to cultivate and develop their faculty. Distributing computer science education institution-wide through enhanced curriculum offerings and faculty training offers a faster option to prevent both short-term gaps in the workforce and intensifying pressures on traditional programs.
Different approaches are emerging to embed data literacy and other cornerstones of computer science throughout academic programming. For example, Colby College offers five different interdisciplinary computing majors in which students learn about theater and dance, music, the environment, biology and psychology through a computational lens. Elsewhere, institutions such as the University of Virginia, the University of Texas at Austin and the University of North Carolina at Charlotte are forming interdisciplinary schools of data science. The idea is to forge partnerships and relationships across university communities from a central hub, giving students opportunities to cultivate their data interests.
Institutions are also looking to shared services to support professional development and student learning. Hundreds of higher education faculty, librarians, research staff and graduate students across a range of disciplines have benefited from training through the nonprofit ITHAKA’s Constellate learning platform, aimed at broadening access to text- and data-analysis knowledge. Instructors build their skills through professional development courses in everything from data cleaning, visualization, and analysis, to text analysis courses like Python Basics and state-of-the-art language models and generative artificial intelligence. Constellate uses an open, flexible, and scalable lab infrastructure, and lessons are available as Open Educational Resources for instructors to adapt, reuse, and remix for their own communities. After two months of Constellate training, most educators report that they’re ready to teach text analysis.
By releasing skills including data literacy from the silo of computer science, colleges and universities can spread a foundational competency across campus while preparing students for the jobs of the future. We are in a transformative moment for data skills and technological advances. Every discipline, every faculty member, and every student can benefit from being data literate. The questions facing every institution now are: Will computer and data literacy education be siloed off into a few lucky departments? Or will you find, retain, and grow the necessary talent for the benefit of the whole student community?
Nathan Kelber is the Educational Manager for Constellate and Director of the Text Analysis Pedagogy Institute.