This article is published in the April 2020 issue.

The Many Pathways to Graduate Education in Computing

Horizontal bars showing percentages of undergraduate students’ immediate plans after graduation by their highest degree intentions and underrepresented minority status.


A healthy computing research workforce is critical for driving innovation in computing and technology. Advances in basic computing research is linked to many major technological advancements [1]. Without a wide pool of new researchers to sustain this workforce, it is inevitable for innovation in computing to slow down. Beyond maintaining a large pool of researchers, it is also important to ensure that these researchers reflect the diversity of the population they are embedded in.

We analyzed students’ career plans immediately after receiving their bachelor’s degree and their intentions for the highest degree they plan to pursue. Focusing on those students who are interested in pursuing a graduate degree, the analysis found that there are multiple pathways students envision taking to receive a graduate education. Our analysis examined students who are underrepresented in computing (URMW) and those who constitute the majority separately to identify whether there are any differences in their career plans.

The graphic demonstrates that only about a third of the students who are interested in ultimately getting a master’s degree plan on entering graduate school right after they graduate while more than 60% intend to first enter the workforce. Our analysis showed that there was not a difference between URMW and majority students in this regard.

Results for those students who are planning on getting a doctorate degree also showed a variation in how they anticipate reaching this goal. Of note, we found that URMW and majority students differ in terms of the direction of their path to a doctoral degree. While students who are underrepresented in computing are more likely to first apply for a master’s degree, students who are the majority in terms of their racial/ethnic and gender identity are more likely to take a direct path to a doctoral program. Still, about a third of both groups plan on entering the workforce once they receive their bachelor’s degree.

The reasons behind how students formulate these plans and factors that explain difference between students of different racial/ethnic and gender identities require further analysis. Additionally, while these students report intending to ultimately pursue a graduate degree, it is unclear whether they actually move from the workforce to graduate school or continue on to a doctoral program after receiving their master’s degree.

[1] National Research Council. 1995. Evolving the High Performance Computing and Communications Initiative to Support the Nation’s Information Infrastructure. Washington, DC: The National Academies Press.


  • The survey data used in this graphic were collected during fall 2018 by CERP via the Data Buddies Project.
  • Students were asked to report their plans immediately after they graduate and their highest degree intentions. Immediate plans included in the ‘Other’ category are: Apply for a certificate program, Create a start-up, Take a break from work and school, Another plan not best described above
  • The survey sample contains 5,731 undergraduate students in computing fields. Only 3,389 students who reported intending to get a graduate degree are included in this graphic.
  • Majority: White men and Asian men; URMW: Black/African American men, Hispanic/Latino men, Native American men, Native Hawaiian/Other Pacific Islander men, all women.
  • *, +, and ~ indicate statistically significant difference in the respective proportions between majority and URMW (p < 0.05)
  • Percentages may not add up to 100% due to rounding error

horizontal CERP logoThis analysis is brought to you by the CRA’s Center for Evaluating the Research Pipeline (CERP). CERP provides social science research and comparative evaluation for the computing community. Subscribe to the CERP newsletter here.

This material is based upon work supported by the National Science Foundation under grant numbers (CNS-1246649, CNS 1840724, DUE-1431112, and DUE 1821136). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.