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![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](https://image.slidesharecdn.com/redfish-bluefishdonecompressed-191210205152/75/Red-Fish-Blue-Fish-Reexamining-Student-Understanding-of-the-Computing-Disciplines-10-2048.jpg)
![Some [academic] borders are so strongly defended
as to be virtually impenetrable; others are weakly
guarded and open to incoming and outgoing traffic:
but in general a considerable amount of poaching
goes on across all disciplines
Becher, T. and Trowler, P. R. (2001). Academic tribes and territories, Second Edition.](https://image.slidesharecdn.com/redfish-bluefishdonecompressed-191210205152/75/Red-Fish-Blue-Fish-Reexamining-Student-Understanding-of-the-Computing-Disciplines-33-2048.jpg)
Uploaded byRandy Connolly
Red Fish Blue Fish: Reexamining Student Understanding of the Computing Disciplines
This paper updates findings from a multi-year study on computing students' understanding of various computing disciplines, comparing student knowledge to official ACM definitions. The study involves multiple phases of surveys across different universities, revealing insights on disciplinary fit perception among students and faculty, and indicating that students grasp the interconnectedness of computing areas better than faculty. Results suggest a need for improved guidance for students regarding computing disciplines to enhance their academic satisfaction and career choices.
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Red Fish Blue Fish: Reexamining Student Understanding of the Computing Disciplines
- 2. ABOUT THE PAPER This paperupdates the findings of a multi- year study that is surveying major and non- major students’ understanding of the different computing disciplines. GET STARTED
- 3. MOTIVATION Do computing studentshave an understanding of the ACM computing disciplinary identities and boundaries and to what degree does student understanding mirror the official ones defined by the ACM.
- 4. Courte, J. andBishop-Clark, C. 2009. Do students differentiate between computing disciplines?. In Proceedings of SIGCSE '09. 2009 Uzoka, F-M., Connolly, R., Schroeder, M, Khemka, N, and Miller, J. (2013). Computing is not a rock band: student understanding of the computing disciplines. In Proceedings of SIGITE '13. 2013 BACKGROUND
- 5. Phase 1 Fall 2012 Paperquestionnaires given to MRU first year CS, IT, and non-major students. Phase 3a Winter 2016 Online (SurveyMonkey) questionnaires given to MRU and BYU students. Phase 2 Winter 2014 Paper questionnaire modified slightly and given to first-year and upper-year MRU students. TIMELINE
- 6. Phase 4a Fall 2016/Winter2017 Questionnaires distributed to other universities: Univ of Cincinnati, New Hampshire, ... Phase 3b Spring 2016 Online (SurveyMonkey) questionnaires given to DePaul and Mbarara Univ students. Phase 4b Fall/Winter 2017 Questionnaires distributed to wider range of computing faculty. TIMELINE
- 7. RANDY CONNOLLY Mount RoyalUniversity Mathematics & Computing OUR TEAM JANET MILLER Mount Royal University Counselling MICHAEL UZOKA Mount Royal University Mathematics & Computing MARC SCHROEDER Mount Royal University Mathematics & Computing
- 8. BARRY LUNT Brigham Young University CRAIGMILLER DePaul University ANABELLA HABINKA Mbarara University ???? Anywhere
- 9. DEGREE OF FITOLD AND NEW QUESTIONS FACULTY RESPONSES METHODOLOGY
- 10. ORIGINAL STUDY [2009] Inthe 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
- 11. Our questionnaire had demographic-relatedquestions and then 31 discipline/task questions. SO WE DID
- 12. FACULTY RESPONSES We determined bestfit by having faculty (n=13) from four universities (and four different computing disciplines) fill in the same survey as the students. We then used their responses to construct the disciplinary best fits.
- 13. RESULTS After filtering outuncompleted surveys, our analysis was able to use 668 completed North American surveys.
- 14.
- 15.
- 16. STUDENT VS FACULTY RESULTS Designs hardwareto implement communication systems Uses new theories to create cutting edge software
- 17. STUDENT VS FACULTY RESULTS Combines knowledgeof business and technology. Manages large scale technological projects.
- 18. STUDENT VS FACULTY RESULTS Develops ormaintains web sites. Manages a team of software developers.
- 19. RANK ORDER ANALYSIS This analysismethod is especially well suited for interval data lacking objective measures of correctness.
- 20. Discipline Match Distribution Thematch between student and faculty rankings was remarkably close.
- 21.
- 22. CS vs ITSTUDENTS Examining our one-way ANOVA analyses of the role that the students’ program of study had on their task scores, we discovered that one of the biggest differences was that between CS and IT students.
- 23. CS VS IT STUDENTS Utilizes theory toresearch and design software solutions. Manages a team of software developers.
- 24. CS vs ITSTUDENTS 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.
- 25. DISCIPLINARY CLUSTERS The 31 questionswere grouped into five categories representing best-fits with each of the computing disciplines. Cluster scores were then calculated for each student participant by adding together the target discipline rating for each question assigned to this cluster.
- 26. DISCIPLINARY CLUSTERS ANOVA showed statistically significant(p<0.05) differences in all five cluster areas.
- 27. CLUSTER ACCURACY An average ofall 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.
- 28. CLUSTER ACCURACY Post-hoc (Bonferroni) analyses showedthat SE and CS students scored significantly lower than their peers in other disciplines.
- 29. DISCIPLINARITY ACM FRAMEWORKCOUNSELLING DISCUSSION
- 30. DISCIPLINARY FIT By allowingstudents to specify a degree of disciplinary fit, our study showed that by and large students are able to get their discipline matches surprisingly close.
- 31. DISCIPLINARY FIT Student responsesmirrored faculty responses in direction of fit but not in exact quantity of fit. This could be interpreted as meaning the students are less certain about disciplinary fit than the faculty.
- 32. Perhaps students areactually more cognizant than faculty of the uncertain fit between the different computing disciplines and real-world computing tasks and thus see disciplinary boundaries as being permeable. STUDENTS University faculty live and breathe disciplinary silos, so it is natural that they would see disciplinary fit in a more extreme manner than students. FACULTY
- 33. Some [academic] bordersare so strongly defended as to be virtually impenetrable; others are weakly guarded and open to incoming and outgoing traffic: but in general a considerable amount of poaching goes on across all disciplines Becher, T. and Trowler, P. R. (2001). Academic tribes and territories, Second Edition.
- 34. ACM FRAMEWORK Our dataseems to be in line with the ACM’s (2005) theoretical framework.
- 35. ACM FRAMEWORK We triedto 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. Both students and faculty recognized that CS and SE shared best fit with both the CS and SE tasks. Similarly, students and faculty believed that IS and IT have overlapping task identities. Our data also indicated that IS, IT, and SE have some overlap.
- 36. COUNSELLING Our study resultsalso provides guidance for counsellors and advisors.
- 37. TWO-STEP INTERVENTION PROCESS In the firststep, 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.
- 38.
- 39. LIMITATIONS OBVIOUS QUESTIONS? NOTENOUGH “IS” STUDENTS MORE FACULTY RESPONSES SURVEY FATIGUE
- 40.
- 41. KNOWLEDGE OF DISCIPLINES Studentsand faculty share a general understanding of the computing disciplines, and for students, discipline understanding becomes more refined as they proceed through their undergraduate experience. DISTINGUISH SE/CE + IT/IS To support incoming students and prospective students in their career choice, our data shows that guidance practitioners will need to provide more specific information about the CS/SE distinction and the IT/IS distinction.
- 42. Examining the ACMCurricula Reports for each discipline, we could not help noticing that the ACM IT, IS, and CE model curriculum reports each have a section right at the start reflecting on their discipline’s relationship to the other five disciplines. The CS and SE reports do not! STARTING WITH CURRICULM REPORTS Within the computing disciplines, it appears that the SE and CS students could benefit especially from having more knowledge about the other computing disciplines. SE + CS NEED MORE
- 43. FINAL THOUGHTS Disciplinary boundariesare not immutable but are socially constructed (and thus can change over time) Nonetheless, we believe that if computing students have a realistic understanding of the identity and boundary of not only their own discipline but also that of neighboring disciplines, it is likely to improve their ultimate satisfaction with their discipline.
- 45. Randy Connolly Janet Miller MountRoyal University, Calgary, Canada Faith-Michael Uzoka Marc Schroeder Barry Lunt Annabella Habinka Craig Miller Brigham Young University DePaul University Mbarara University, Uganda