It’s just after the lunch bell on a sunny day in late October, and students are queuing up outside of the counselling office hoping to ask “just a quick question” about what to take in university, or what to do with their lives after graduation. Right around the time that university early admission deadlines hit, and again near graduation, students experience an urgent need to get career things figured out. We know these are not simple questions, and quick fixes come at a high price – often too high when career satisfaction or personal wellness are at stake.
The free guidebook we’re going to talk about in this article won’t help solve all of your students’ problems, but it will help to untangle the complexity of one major field of study – Computing. It’s called “Computing Careers & Disciplines: A Quick Guide for Prospective Students and Career Advisors” (Connolly, Miller, & Uzoka, 2025), and thanks to the generous support of CERIC, it’s available as a free PDF download in both English and French (ceric.ca/publications). https://ceric.ca/publications/computing-careers-disciplines-a-quick-guide-for-prospective-students-and-career-advisors-3rd/
The process of selecting a career or a pathway of higher education is complex. The Social-Cognitive Career Theory (SCCT) model boiled that complexity down to a triad of career decision factors: interest, self-efficacy, and outcome expectancy. In the original model, interests (i.e., is a client interested in this field) and self-efficacy (the client’s belief they will succeed in the field) were considered the key determinants of one’s career choice, but recent research focusing on science, technology, engineering, mathematics (STEM) and computing fields have discovered that outcome expectancy and background knowledge are even more important (Nugent, et al. 2015; Stipanovic, Stringfield, & Witherell, 2017). Career expectations can be framed as our “perception of certain careers based on their perceived monetary, social, and self-satisfaction outcomes.” Outcome expectancy is particularly influenced by role models and visualizations. That is, based on what I know of this career, can I, as a young person, see myself in that role?
Computing is one of those career interest areas where students often say they feel quite confident about their understanding of what the job entails. After all, they’ve grown up with computers, gaming consoles, and phones, so when they apply to study something like computer information systems, they know what they’re getting in to. The problem is, they actually don’t (Connolly, et al., 2016; Courte & Bishop-Clark, 2009), and these erroneous outcome expectations can lead to being stuck in a program that doesn’t suit their interests as course curriculums are often so distinct that switching programs often means starting over. Making the “wrong” career choice can cost time, money, and momentum.
High school counsellors have told us that they get asked about computing careers quite often. Late last year along with our colleague Dr. Michael Uzoka, we launched a survey of high school counsellors (Miller, Connolly, & Uzoka, 2024) to find out what students wanted to know about the field of computing. Preliminary findings (n=25) suggest that more information about computing careers is needed. Forty-three percent of respondents said that they think students’ interests in computing careers is on the rise, with many indicating that they are asked about computing careers “often” or “very often”. Common questions from students fell into four main categories:
- Employment Prospects (demand, income, trends, entry into the field, and ways to apply education across disciplines)
- Educational Pathways (Where should I study? What program is best? College or University?)
- University Entrance Requirements (competitive averages, HS course requirements, pathways in)
- Questions about Experiential Learning (hands-on experience, coop programs, job-routes, coding camps)
As you likely expect, the most popular computing job areas of interest included game development, game design, and of course – computer science. Counsellors felt that information systems, information technology, cybersecurity, and data science were among those areas of computing that are the least understood. These occupational areas are growing in demand, but students don’t yet understand what credentials in these fields can lead to. Computing is further complicated by the fact that a variety of job titles are used to describe similar positions, and even programs of study can go under a variety of names.
To assist with navigating through this field, the Association of Computing and Machinery (ACM, 2020) has articulated seven distinct computing disciplines, with model post-secondary curriculum developed for each area. They are: computer engineering (CE), computer science (CS), cybersecurity (CY), data science (DS), information systems (IS), information technology (IT), and software engineering (SE). The ACM recognizes that while there is topic overlap across the disciplines, each discipline nonetheless has a unique and distinct academic identity. While a university program in one of the ACM disciplines is an important way to achieve a computing career, there are many other pathways: two-year diplomas, three-year associate degrees, post-degree certificates, code camps, and on-line courses, all of which can potentially lead to a computing career. The Guide highlights each discipline, then lists a variety of educational pathways and programs from across Canada that can lead into that field. The “Computing Careers & Disciplines: A Quick Guide for Prospective Students and Career Advisors,” is oriented around three key questions:
- Why should you consider computing as a career?
- What kinds of computing jobs are out there?
- What kind of educational pathways can one take to achieve one of those computing jobs?
Laid out like a graphic novel, using pictures more than words, professor/designer/lead author Randy Connolly has created a visual story for each ACM computing discipline. The illustrations encourage students to develop outcome expectations informed by data and further supported by alumni testimonials.
The “How do I get there” question is answered chiefly with a detailed look at the seven main ACM disciplines (CE, CS, CY, DS, IT, IS, SE) and a more cursory look at several other computing specialties (bioinformatics, network technology, game development, artificial intelligence, and web development). To help explain the differences in these seven computing disciplines, the guide makes use of an urban/architectural metaphor. Why? Within career advising, spatial and journey metaphors such as paths, roads, buildings, and maps are commonly encountered (El-Sawad, 2005). Within computing the term “architecture” is used to refer to a wide range of topics, from hardware topologies, to software constructs, to chip design. Here it’s used as a more literal metaphor. Each discipline was given its own city district that visually encapsulates how the discipline is understood by ACM (and thus the “outside” or official view) of the discipline.
For instance, to represent the electrical engineering foundation of software engineering, Connolly made use of an illuminated night-time downtown view. Cybersecurity is represented within an airport, while IS with its focus on business usages of computing, is unsurprisingly represented as a financial district. CS, which is often at the forefront of computing innovations, is situated in a futuristic Mars space station. This “outside” view of each discipline is combined with text from the 2020 ACM Computing Curricula Overview Report (ACM, 2020), as can be seen in Figure 1.
Walker (2019) recently wrote that “prospective students (or students at the start of their academic career) likely have little sense of the full range of opportunities and options within the computing discipline” and that they “rarely have considered … what work in the field might entail.” (p.11) So while the “outside” view corresponded to the educational definition of the discipline, our “inside” view corresponds to the typical career or work tasks performed by graduates within their field. And as can be seen in the sample inside view pages shown in Figure 2 we tried to capture the overlaps between disciplines via the bottom-of-the-page reflections of the persona characters from other disciplines.
For each discipline, on the job activities, core courses, sample degrees, and alternative education pathways are provided from across Canada. The charts from the ACM 2020 Computing Curricula Reports were also updated visually, and career advisors have told us that these are particularly useful tools for individualized counselling sessions or information workshops.
Lyon and Denner (2017) noted that support from advisors and counsellors is helpful for all students, but essential for individuals who travel on less common, more convoluted career pathways. We hope that this Guide will be of benefit to your work, making it easier to describe these fields and support students to develop accurate pictures of their possible futures in the dynamic and exciting world of computing.
References
Alexander, P.M. et al. (2011). Factors affecting career choice: Comparison between students from computer and other disciplines. Journal of Science Education and Technology, 20(3), 300–315.
Association for Computing Machinery, (2020). Computing Curricula 2005 – The Overview Report. 200; http://www.acm.org/education/curricula-recommendations. Accessed 2020 April 16.
Connolly, R., Miller, J., & Uzoka, F.M. (2025). Computing Careers & Disciplines: A Quick Guide for Prospective Students and Career Advisors. Ottawa, Canada: CERIC
Connolly, R., Miller, J., Uzoka, F-M., Lunt, B., Schroeder, M., Miller, C.S., & Habinka, A. (2016). Red fish blue fish: Reexamining student understanding of the computing disciplines. In Proceedings of the 17th annual conference on information technology education, (SIGITE 2016), ACM:
New York, NY, 115–120.
El-Sawad, A. (2005). Becoming a lifer? Unlocking career through metaphor. Journal of Occupational and Organizational Psychology, 78(1), 23–41. DOI: https://doi.org/10.1348/0963117904X22917.
Lyon, L.A. & Denner, J. (2017). Community colleges: A resource for increasing equity and inclusion in computer science education. Communications of the ACM, 60(12), 24–26.
Miller, J., Connolly, R., & Uzoka, FM. (2024). High School Counsellors and the Computing Disciplines: What do your students want to know? study in progress.
Nugent, G., Barker, B., Welch, G., Grandgenett, N., Wu, C., & Nelson, C. (2015). A Model of Factors Contributing to STEM Learning and Career Orientation. International Journal of Science Education, 37(7), 1067–1088. DOI: https://doi.org/10.1080/09500693.2015.1017863.
Stipanovic, N., Stringfield, S., & Witherell, E. (2017). The Influence of a Career Pathways Model and Career Counseling on Students’ Career and Academic Self-Efficacy. Peabody Journal of Education. 92(2), 209–221. DOI: https://doi.org/10.1080/0161956X.2017.1302217.
Walker, H.M. (2019). What kind of computing program(s) should my school offer? ACM Inroads, 10(4), 10–14. DOI: https://doi.org/10.1145/3362808.
THE AUTHORS
Janet Miller, PhD., Registered Psychologist (AB & ON), Professor, Student Counselling, Mount Royal University
Professor Randy Connolly, Department of Mathematics & Computing, Mount Royal University
