I Didn’t Know That Was Cheating: Student and Instructor Views
on Academic Integrity in Computer Science
Jaime Paredes Paez
Michelle Cheatham
Leanne Wu

jaime.paredespez@ucalgary.ca
michelle.cheatham@ucalgary.ca
lewu@ucalgary.ca
University of Calgary
Calgary, Alberta, Canada

Abstract
Cheating has been a perennial problem in universities for centuries,
and the rise of information technologies that ease the process is
exacerbating the situation. In some fields, such as computer science,
practitioners routinely build on code or other resources available
online in the course of their normal workflow. The view taken on
this type of action in an academic context may vary from instructor to instructor and course to course. This creates a potentially
confusing environment for students. This work surveys a group
of instructors and students on their opinion regarding the level of
academic dishonesty inherent in 13 different scenarios, as well as
the reasons that each group believes underlie students’ decision to
cheat or to refrain from doing so.

Keywords
computer science education, academic integrity, academic dishonesty, cheating, plagiarism
ACM Reference Format:
Jaime Paredes Paez, Michelle Cheatham, and Leanne Wu. 2025. I Didn’t
Know That Was Cheating: Student and Instructor Views on Academic
Integrity in Computer Science. In The 27th Western Canadian Conference
on Computing Education (WCCCE’25), April 28–29, 2025, Calgary, AB,
Canada. 5 pages. https://doi.org/10.60770/a8cy-p868

1 Introduction
Ask any university professor about their experiences with academic integrity violations, and you will almost certainly be on the
receiving end of a lengthy rant about the issue. The prevalence of
instances of academic dishonesty in higher education has consistently been found to be very high. Surveys have found dishonesty
rates as high as 70 [10], 85 [11], or even 95% [7] at some universities.
The frequency of academic dishonesty, likely combined with the
sensitivity and immediacy of the topic for those in the research
This work is licensed under a Creative Commons Attribution 4.0 International (CCBY 4.0) license. For all other uses, contact the authors.
WCCCE ’25, April 28–29, 2025, Calgary, AB
© 2025 Copyright held by the owner/authors
https://doi.org/10.60770/a8cy-p868

and education professions, has resulted in a large body of research
into many aspects of the issue. For example, existing work has
considered how the prevalence of integrity violations varies by
gender [5], ethnicity [3], year of study [11], and field of study [6].
Other efforts have considered the reasons why students cheat [1, 14]
and what might be done to prevent it [2, 8].
Some issues of note in this research are:
It is difficult to reproduce the results in many cases. For
example, some results [5] show a significant difference in the rates
of academic integrity violations between males and females, while
others, e.g. [7], show no such difference. Results also vary based
on field of study and the type of institution at which a survey
was conducted. Much published work has a quote such as the
following: Future studies may include replications of our findings
in different types of institutions [3], with "types of institutions"
replaced by "field of study", "cohort of students", etc. The difficulty
in replication may be because researchers have yet to identify key
variables that are tied to the likelihood of academic dishonesty. It is
of particular concern because many instructors refer to universitylevel guidelines when discussing academic integrity in their syllabus
or other course documents, but the one-size-fits-all nature of those
guidelines is unlikely to be sufficient.
Defining terms is challenging. For a survey containing questions like "have you ever committed an academic integrity violation?" to produce meaningful results, both the researchers conducting the survey and the participants must have a shared understanding of the meaning of that term. However, there are often significant
differences of opinion on what constitutes an academic integrity
violation among instructors in different fields of study and even
among colleagues in the same department [2]. This confusion leads
to the common student complaint that "I didn’t know that (what I
did) was wrong." It may also be a factor in instructors deciding not
to report an academic integrity incident, since they may be unsure
that their colleagues will agree with their assessment of the behavior as dishonest [8]. One approach to dealing with this problem that
has emerged in the literature is to ask about particular scenarios
[3, 6, 13]. For example, "what percentage of your classmates do you
think would share their homework solution with another student
if asked?" rather than "what percentage of your classmates do you
think cheat on their homework?"
Computer Science may be a particularly challenging field
with respect to academic integrity issues. Things specific to

WCCCE ’25, April 28–29, 2025, Calgary, AB

computer science, such as a reliance on online code repositories
[6], may combine with student and instructor views on digital
versus analog cheating to make academic integrity issues more
confounding for students in this field [3]. Section 2 of this paper
goes into more detail on these issues.
In this work, we survey computer science students and instructors regarding potentially dishonest academic practices relevant to
the field. Our goal is to answer the following research questions:
• Do students and instructors share a common understanding
in and between groups of what types of actions constitute
an academic integrity violation?
• What types of actions generate the most uncertainty regarding academic integrity? Does this vary by group?
• Are the above results significantly impacted by demographic
characteristics such as gender, years of experience, etc.?

2

Related Work

As mentioned previously, there is a great deal of existing work
on the topic of academic dishonesty. A survey of literature in this
area can be found in [9], and a collection of recent articles on the
subject that illustrates the variety of viewpoints being considered
can be found in [4]. Because our current work focuses specifically
on the views of computer science students and instructors regarding
academic integrity violations, we focus narrowly on that topic in
this section.
A key aspect of computer science that potentially distinguishes
it from other fields of study and practice is the commonplace reliance on digital tools and artifacts, and public information stores.
Programming today is frequently done within an integrated development environment that supports auto-completion of coding
statements, seamless lookup of online documentation and “cookbook” code snippets, and generative AI-powered code suggestions.
Additionally, there are many online learning and help sites devoted
to helping people learn to program and fix bugs in their code. Popular examples include StackOverflow and GeeksForGeeks. StackOverflow in particular is a common venue for students to post nonworking code and receive feedback and/or corrected code. Another
characteristic of modern software development is the avoidance of
re-inventing the wheel. Much application development involves
the piecing together of existing libraries and other pieces of code in
novel ways, rather than coding the entire application from scratch.
As a result, there is a large amount of functional source code freely
available on public repositories such as GitHub. There is some speculation that students “perceive anything that can be found on the
internet to be common knowledge” and that students therefore do
not see any issue with using the material without attribution [12].
Computer science’s reliance on digital artifacts may impact students’ and instructors’ views of what constitutes an academic integrity violation. In 2020, Ina Blau and her colleagues at Open
University in Israel conducted a survey of 1482 students and 42
faculty members regarding the appropriateness and severity of actual penalties imposed by that university’s disciplinary committee
[3]. Study participants were from the “humanities, social sciences,
life and natural sciences, and exact sciences (including computer
science).” The authors found that faculty perceived academic integrity violations to be more grave than did students. In most cases,

Paez et al.

both groups found analog integrity violations to be worse than digital violations, with the exception of “digital facilitation” (assisting
someone else in cheating, plagiarizing, or fabricating data). Both
groups suggested more severe penalties for analog versus digital
offenses, including for facilitation.
Recently, Banson et. al. conducted a scenario-based survey specific to computer science students [6]. They surveyed 160 students
(no instructors) from 15 undergraduate computer science courses.
Participants were presented with 15 scenarios involving “collaboration or help seeking” in CS courses and asked to rank their
acceptability on a scale of 0 to 100. The authors point out that
standard dishonesty policies are typically black and white, and yet
students frequently rated some scenarios as much more acceptable
than others. Furthermore, some scenarios, such as “asks a friend
about a homework assignment using their actual code as a reference, and the friend gives tips based on the student’s questions”
generated significant disagreement amongst the respondents, suggesting some uncertainty regarding expectations. This uncertainty
was present even in scenarios that most computer science professors explicitly forbid, such as “asks a friend about a homework
assignment, and the friend gives tips by revealing their code.”
The next two sections outline our own scenario-based survey
administered to computer science students and instructors and the
results, which we will contextualize in relation to the previous work
described here.

3

Methodology

Along with several other researchers, we have adopted the approach
of asking about specific scenarios rather than abstract questions
to explore individuals’ attitudes towards academic integrity issues
[3, 6, 13]. This allows us to avoid issues related to a difference in
understanding of the meaning of terms like “academic integrity
violation” and “plagiarism” between respondents. It also enables
us to focus on concrete circumstances in which computer science
students routinely find themselves.
The research team collaboratively developed 13 hypothetical
scenarios by drawing on critical issues that have been frequently
observed within the Faculty of Computer Science. For example, one
scenario is “While working on a programming assignment, you find
a website with material that addresses the brief for the programming
assignment. You copy two or three important lines of code from
the website and use them in your code. You do not acknowledge
the source of this code.” These scenarios were often highlighted
by instructors in one-on-one discussions, faculty meetings, and
department workshops. They largely overlap with those found in
[6] but are tailored to our institution and department.
Both students and instructors were asked about this set of scenarios, with minor wording changes for clarity. Students rated how
acceptable each action is on a 5-point Likert scale from “acceptable”
to “not acceptable”, while instructors were asked if they consider
the action a breach of academic integrity (on a 5-point scale from
“definitely yes” to “definitely no”.
Both groups were also asked about common reasons behind
academic integrity violations. These reasons again reflect the experiences and hypotheses of faculty in our department but largely
overlap with those discussed in the literature, such as [6]. Each

I Didn’t Know That Was Cheating: Student and Instructor Views on Academic Integrity in Computer Science

group was given a list of the same 15 potential reasons that a student might cheat (e.g. “not enough time”, “everyone does it”) and
ten potential reasons a student might refrain from cheating (e.g.
“fear of being found out”, “fairness to other students”) and asked to
assess their likelihood on a 5-point scale.
The surveys also contained questions about related issues such
as students’ perceptions of the efforts made by instructors and
the university to prevent cheating and instructors’ approaches to
discouraging academic integrity incidents; however, due to space
limitations, these elements will be discussed in future publications.
Unfortunately, the student survey went through internal review
board approval prior to the rise of generative AI tools such as
ChatGPT, so that topic was not covered in this study.
We recruited a total of 137 undergraduate students and 19 instructors from the Computer Science faculty to participate in our study.
The student cohort included 112 participants from an in-person 200level theory class who completed the survey in November 2022 and
25 participants from a 300-level online theory class who completed
it in March 2023. Instructors were recruited during a departmental
meeting in February 2024 and were given a month to complete the
survey. All participation was voluntary, and students were given the
option to enter a raffle for a $20 bookstore gift card as an incentive.

4

Results

Table 1 shows the mean and standard deviation among the instructors and students regarding the acceptability of each scenario, while
tables 2 and 3 show both groups’ views on the applicability of a set
of reasons to cheat and not to cheat, respectively.

4.1

Scenarios

There was fairly wide variation in the instructor responses regarding the acceptability of different actions: the standard deviation was
approximately a full Likert scale level on six of the 13 scenarios. The
disagreement covered both actions considered generally acceptable,
like modifying an instructor’s solution to a similar problem and
discussing an assignment in a group but writing it up alone, and
those considered unacceptable, such as using two or three lines of
code from a website without attribution and showing a friend their
solution after obtaining a promise from them not to copy it. There
was total agreement among instructors that paying someone else
to solve an assignment is definitely cheating.
The variation among the students’ responses was higher than
among the professors, but generally involved the same scenarios.
For instance, three of the four highest deviations among student
responses were also among the four most variable instructor responses. The exception was that students disagreed more on the
acceptability of showing a friend their solution after obtaining a
promise not to copy it and much less on using a friend’s solution
from a previous term. Students did not come close to total agreement amongst themselves on any scenario.
Opinions on the acceptability of various actions aligned fairly
well across instructors and students. Agreement was strongest on
the acceptability of going to office hours (considered fine) and
posting an assignment solution to GitHub prior to the deadline
(considered unacceptable / probably cheating). Opinions differed

WCCCE ’25, April 28–29, 2025, Calgary, AB

most strongly between the two groups on using a friend’s solution for a similar assignment from a previous term and showing
a friend their solution after the friend promises not to copy it. In
both of these cases, instructors considered the action “definitely
cheating” while students considered it ““unacceptable” rather than
“not acceptable at all”). Interestingly, instructors were more lenient
regarding the acceptability of asking for an 84.4% to be rounded up
to an A than were students (4.63 versus 3.88).
A couple of scenarios (using a friend’s solution from a previous
term and buying a solution on a contracting website) had substantially more variation amongst the student responses than those
of the instructors, but in general, situations that showed division
amongst the instructors did so for students as well, and vice versa.

4.2

Reasoning

The standard deviations indicated that there was less consensus
both within and between the instructors and students regarding the
impetus for students to cheat or to refrain from doing so. However,
there was agreement between the two groups regarding the top
three reasons for cheating (not enough time, workload too high,
and to avoid failing) and on two of the three least likely reasons
(monetary reasons and “everyone does it”). The only difference
amongst these results was that few instructors felt that health issues led students to cheat, while few students believed that laziness
was a common cause. Likewise, there was a fair amount of overlap regarding the reasons students decide not to cheat, with the
ability to get good marks without cheating and pride in working
topping both lists, and religious beliefs, fairness to other students
and not knowing how to go about cheating at the bottom for both.
Interestingly, instructors felt that moral values played a role in not
cheating for many students, while the students suggested that the
penalties if they were caught doing so was a more significant factor.

4.3

Demographics

The data depicts a young, predominantly male, largely domestic,
full-time student body. Among the 137 students’ responses, the
majority were born in 2001 or 2004 (25.5% and 26.3%, respectively).
Approximately two-thirds (65.7%) of the respondents are male, 81%
are domestic students, and 98.5% are enrolled full-time. First-year
students dominate the sample (73%), with progressively fewer students in higher years. Language diversity is evident, with 40.1%
of students speaking only English, while 48.9% speak at least one
language besides English. Motivations for enrollment vary widely:
44.5% express a keen interest in the subject matter, 19.7% are driven
by career prospects, and 15.3% seek to develop coding skills.
Of the 19 instructors surveyed, 21% completed their Ph.D. between 1990 and 1995, and another 21% between 2012 and 2015. A
significant proportion (63%) had not taught at any other institution in the past five years. The range of post-secondary teaching
experience varied: 32% had less than 10 years of experience, 21%
had more than 10 years, and another 21% had over 20 years of
teaching experience. The majority of instructors held the rank of
full professor.
We ran the ANOVA test to look at a wide variety of demographic
variables for both instructors and students, including rank (assistant, associate, full, limited, sessional), track (teacher or research),

WCCCE ’25, April 28–29, 2025, Calgary, AB

Paez et al.

Scenario

Instructors
Mean Std Dev

Students
Mean Std Dev

Round 84.4% up
Parent helps with online test
Parent advises on group project
Modify instructor solution
Use 2 lines of code from website
Discuss assignment in group, write up alone
Ask for deadline extension
Buy solution on contracting website
Use friend’s solution from previous term
Use 2 lines of code from TA
Show friend solution after promise not to copy
Go to office hours
Post solution on GitHub before deadline

4.63
1.32
4.11
3.89
2.11
3.95
4.83
1.00
1.21
4.84
1.63
4.89
1.84

3.88
1.64
4.27
4.05
1.84
3.69
4.58
1.20
2.30
4.44
2.57
4.82
1.80

0.76
0.48
1.05
0.9
1.45
1.08
0.51
0.00
0.42
0.50
1.07
0.46
0.96

1.08
0.87
0.98
1.07
1.12
1.19
0.78
0.66
1.18
0.85
1.24
0.65
1.04

Table 1: Assessment of academic integrity concerns of hypothetical scenarios on a scale of 1 (unacceptable) to 5 (acceptable).

Reason to cheat

Instructors
Mean Std Dev

Students
Mean Std Dev

Not enough time
Workload too high
Else will fail assignment
Else will fail course
Lazy
Everyone does it
To get better marks
Parental pressure
Cannot afford to fail
Assignment too difficult
To help a friend
Health issues
Exams too hard
Afraid of failing
Monetary reasons

4.42
4.37
4.21
4.37
3.05
2.89
3.79
3.22
3.83
3.42
3.47
2.58
2.95
4.05
2.21

2.74
2.90
3.26
3.56
1.71
1.94
2.58
2.01
3.22
2.55
2.27
2.58
2.54
2.74
1.83

1.02
0.83
0.63
0.83
1.13
1.24
0.98
0.81
0.86
1.12
0.91
1.26
1.08
0.71
1.18

1.29
1.37
1.39
1.39
0.96
1.12
1.28
1.31
1.41
1.31
1.23
1.36
1.32
1.39
1.21

Table 2: Assessment of the prevalence of reasons behind
cheating, from 1 (not at all likely) to 5 (highly likely)

years taught (less than 10 to greater than 30 years), gender (selfdisclosed as masculine or feminine), status (Canadian/domestic
or international), language, full- versus part-time students, online
versus in person attendance, and student motivation for enrollment, and discovered no significant statistical difference amongst
these groups (P>0.05) regarding perceptions of acceptability of the
various scenarios.

5

Discussion

Returning to our research questions, the results of this study do
show a common understanding between instructors and students
regarding the acceptability of most of the hypothetical actions from
an academic integrity standpoint and the most common reasons
that students do or do not cheat, in that they generally consider

Reason not to cheat

Instructors
Mean StdDev

Students
Mean StdDev

Want accurate assessment
Pride in work
Can get good marks
Against moral values
Against religious beliefs
Fear getting caught
Never considered it
Don’t know how to
Fairness to other students
High penalty if caught

3.05
4.05
4.42
4.11
2.74
3.84
3.00
1.68
2.37
3.11

4.00
4.10
4.41
3.80
2.55
3.83
3.23
2.71
3.00
4.19

1.08
0.97
0.61
0.81
1.10
0.69
0.94
0.75
0.96
1.24

1.14
1.07
0.92
1.17
1.52
1.25
1.27
1.36
1.41
1.10

Table 3: Assessment of the prevalence of reasons behind
refraining from cheating, from 1 (not at all likely) to 5 (highly
likely)

the same actions acceptable/unacceptable and the same reasons to
cheat or not cheat to be likely/unlikely. These results differ from
those presented in [3], which did find large differences between the
severity of incidents as considered by instructors versus students,
though that work focused on academic integrity incidents that
involved digital resources (e.g. taking code from online rather than
sharing code with a friend), and that may have revealed a stronger
generational gap than the scenarios we used in this study.
None of our results showed a statistically significant difference
for any of the demographic traits considered. This differs from the
work in [11], which found a difference in viewpoint based on year
of study, and [3], which found significant but small differences
based on gender, ethnicity, and language (Hebrew versus Arabic
speaking). Both of these studies had a wider variation among students surveyed in terms of years of study and major subject area
than the work presented here, which may account for some of this
difference.
While the results showed general overall agreement on the
relative acceptability of various actions, the standard deviations

I Didn’t Know That Was Cheating: Student and Instructor Views on Academic Integrity in Computer Science

amongst the responses for both groups indicates that much of this
issue is far from clear cut for either instructors or students. This
matches prior results in the literature, including in [2, 6]. Discussion
amongst faculty members in our department brought up the point
that what is acceptable in one course or for one assignment might
be a clear academic integrity violation in a different context. For
example, if a student is writing a program for a game development
course and uses a depth-first maze navigation algorithm as a small
part of the overall work, that might be considered more acceptable
than if a student in a data structures and algorithms course used
the same code in their assignment, since that might directly thwart
the learning objective of the assignment. This idea is also mentioned in [2]. Because of issues like these, it is very important that
instructors clearly explain the bounds of acceptable behavior, at the
level of individual assignments or at least individual courses. While
more work remains to be done regarding the most effective way
to convey these boundaries to students, it is clear that a one-size
fits all approach such as linking to the generic university academic
integrity guidelines in the course outline or syllabus is unlikely to
be sufficient [2].
As noted previously, students viewed reusing a friend’s solution
on a similar assignment from a previous term as substantially more
acceptable than instructors did. This is inline with prior results in
the literature, such as [11], in which students indicated that instructors should develop entirely new assignments every term (and even
that not doing so may be indicative of laziness on the instructor’s
part). This indicates that it may be helpful for instructors to specifically state that this behavior is unacceptable, as well as explain
the pedagogical reasons for sometimes reusing the same or similar
assignments across terms.
Both groups indicated that lack of time and high workload are
common reasons that students cheat – this may imply that seminars or other resources regarding effective time management and
organizational skills could be useful for students, along with a clear
policy regarding deadline extensions [2].
One particularly interesting aspect of these results is that students considered high penalties for getting caught as a top three
likely reason to avoid cheating, but instructors did not, while instructors considered a student’s moral values to be an in this set
but students did not. This is perhaps a bit too convenient. It is well
established that instructors regularly skip reporting academic integrity violations [2, 6, 11]. Our results indicate that they may view
following through on penalties as unnecessary/ineffective and that
they can instead rely on students’ moral compasses, but the student
responses indicate that this may be wishful thinking.

6

Conclusions

The survey results presented here indicate that while the participating instructors and students had some internal variability in their
views regarding academic integrity issues, the generally roughly
agreed on both the overall assessment of each the acceptability
of various common academic scenarios and the likelihood of various reasons underlying a student’s decision to cheat or not. These
results did not significantly differ based on demographics.
The outcome of this analysis reflects the need for instructors
to clearly communicate what is considered acceptable versus out

WCCCE ’25, April 28–29, 2025, Calgary, AB

of bounds on an assignment-by-assignment, or at least course-bycourse, basis. Instructors should consider making it particularly
clear when and why an assignment from a previous term is being
reused. Seminars or other resources regarding time management
and organizational skills may be particularly helpful for students,
along with clear policies and procedures regarding deadline extensions. Finally, it is important for instructors to report academic
integrity violations when they occur, in order to deter more such
activity in the future.

Acknowledgments
The authors sincerely thank the many individuals within the University of Calgary who assisted in this work, including members
of the CPSC Academic Integrity Working Group: Nathaly Verwaal,
Nelson Wong, Jonathan Hudson, Richard Zhao, Pavol Federl, Wayne
Eberly, and Lora Oehlberg; Sarah Elaine Eaton of the Werklund
School of Education; Bronwen Wheatley of the Department of
Chemistry/Earth, Energy, Environment; Nancy Chibry of the Faculty of Science; and recent graduates Alexanna Little, Samuel Osweiler, and Matthew Buhler. We also thank the University of Calgary
Taylor Institute for Teaching and Learning for financial support
(Grant 2302-27).

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