Presentation at Cannexus 2018 in Ottawa in which we discussed the results of our three-year research project on student understandings of the computing disciplines and described the 32-page full-color booklet for advisers and prospective students.

1. Helping Prospective Students Understand the
COMPUTING DISCIPLINES
2018 – CANNEXUS, Ottawa

2. PROF. RANDY CONNOLLY
Mount Royal University
Mathematics & Computing
DR. JANET MILLER
Mount Royal University
Student Counselling

3. FAITH-MICHAEL
UZOKA
MARC SCHROEDER BARRY LUNT
Brigham Young
University
CRAIG
MILLER
DePaul
University
ANABELLA
HABINKA
Mbarara University
Mount Royal
University
Mount Royal
University
Mbarara
University
Science &
Technology

4. OVERVIEW
COMPUTING CAREERS
TODAY
UNDERSTANDING OF
COMPUTING DISCIPLINES
GUIDE FOR ADVISORS &
CAREER COUNSELLORS
QUESTIONS +
WILD CHEERING

5. LET’S GET STARTED

6. C O M P U T E R
CAREERS
TO D AY

7. COMPUTINGFIRST WORDS THAT COME TO MIND?
If you have a client who is interested in
a career in computing, what kinds of
programs & jobs do you think of?

9. AUTOMATION AND AI

10. CLOUD COMPUTING

11. INTERNET OF THINGS

12. SECURITY

13. BIG DATA

14. GIG ECONOMY

15. STUDENT
UNDERSTANDING
OF THE
COMPUTER
DISCIPLINES

18. DO STUDENTS UNDERSTAND
THESE DISTINCTIONS?

19. DEGREE OF FIT OLD & NEW
QUESTIONS
STUDENT &
FACULTY
RESPONSES
METHODOLOGY

20. PROGRAM OF
STUDY
After filtering out
uncompleted
surveys, our
analysis was able to
use 668 completed
North American
surveys.
RESULTS

21. YEAR OF
STUDY

22. ORIGINAL STUDY [2009]
In the original C&BC study, students
were given 15 task descriptions and
for each task they had to indicate
which of the five disciplines was the
best fit for that task.
OUR STUDY
To address that drawback, our study
allowed the participants to choose
how much each task fit with each of
the five disciplines.
X
X
X
XX
DEGREE OF FIT

23. STUDENT VS. FACULTY RESULTS
Designs hardware to implement
communication systems
Uses new theories to create
cutting edge software

24. Combines knowledge of
business and technology.
Manages large scale
technological projects.
STUDENT VS. FACULTY RESULTS

25. Develops or maintains
web sites.
Manages a team of
software developers.
STUDENT VS. FACULTY RESULTS

26. RANK ORDER
ANALYSIS
This analysis method is
especially well suited for
interval data lacking objective
measures of correctness.
The match between
student and faculty
rankings was
remarkably close.

27. PROGRAM
AND
DISCIPLINE
DIFFERENCES

28. ANOVA analyses looking at students’ program of study and their
task scores, revealed significant differences
between CS and IT students.

29. Utilizes theory to research
and design software
solutions. Manages a team of software
developers.
CS VS. IT STUDENTS

30. CS vs IT STUDENTS
Tightly-defined impermeable
boundaries are characteristic of
well-established and convergent
disciplinary communities, while
newer, more epistemologically open-
ended disciplines are often
characterized by broader, more
permeable boundaries.
The IT students were much more likely than
the CS students to believe a given task
could be handled by multiple disciplines.

31. DISCIPLINARY CLUSTERS
The 31 questions were
grouped into five “best-fit”
categories.
Cluster scores were then
calculated for each student
participant by adding
together the target discipline
rating for each question
assigned to this cluster.

32. DISCIPLINARY
CLUSTERS
ANOVA showed
statistically
significant
(p<0.05)
differences in all
five cluster areas.

33. CLUSTER
ACCURACY
An average of all discipline
cluster scores yielded a
total accuracy score, and
again significant differences
among students from the
various programs was
found,
F (6, 350) = 6.178, p = 0.00.

34. CLUSTER
ACCURACY
Post-hoc (Bonferroni)
analyses showed that
SE and CS students scored
significantly lower than
their peers in other
disciplines.

35. Our data seems to be in line with the ACM’s
(2005) theoretical framework.
ACM FRAMEWORK

36. We tried to re-visualize this ACM
diagram using our cluster data,
and found that our results
extend the ACM groupings.
The CE grouping appears to have
the most clearly defined task
identity.

37. OUR CLIENTS NEED HELP IN FINDING
THEIR DIRECTION…

38. KNOWLEDGE OF DISCIPLINES
Students and faculty share a
general understanding of
the computing disciplines,
and for students, discipline
understanding becomes
more refined as they
proceed through their
undergraduate experience.

39. To support clients in
their career choice,
our data shows that
career practitioners
will need to provide
more specific
information about the
distinction between
CS/SE and IT/IS
disciplinesDISTINGUISH
SE/CE + IT/IS

40. TWO-STEP
INTERVENTION
PROCESS
In the first step, we should help
students to identify the general
computing area that is of most
interest (CE, CS/SE or IT/IS).
In the second step, further
define interests and clarify
understanding within each of
those areas.

41. GUIDE
ADVISORS and
CAREER
COUNSELLORS
F O R

44. OVERVIEW OUTSIDE INSIDE DETAILS

50. WHAT ASPECT
OF COMPUTING
WILL I FOCUS ON?
Most computing jobs fall
into one of the following
general categories

54. Helping Prospective Students Understand the
COMPUTING DISCIPLINES
2018 – CANNEXUS, Ottawa

55. RIZ IBRAHIM, Executive Director

56. Randy Connolly Janet Miller
rconnolly@mtroyal.ca jbmiller@mtroyal.ca