Helping Prospective Students Understand the Computing Disciplines

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.

Transcript

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. COMPUTING FIRST 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 X X 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 disciplines DISTINGUISH 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