Digital Technology and Academic Integrity
Running head: DIGITAL TECHNOLOGY AND ACADEMIC INTEGRITY
Digital Technology and Academic Integrity
Thomas Calabrese, Jason Stephens, Michael Young
University of Connecticut
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Digital Technology and Academic Integrity
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Abstract
Given the popular media’s attention to an apparent rise in digital cheating, it is important
to educators that those claims be measured, tested, and qualified. An on-line study of 116
college students was conducted. The study focuses on determining whether
undergraduates report engaging in forms of digital cheating more frequently than
traditional (non-technical) forms of cheating. Additionally, the study investigates the
relations between students’ engagement in digital cheating, their technical knowledge,
and their involvement in Internet based socializations (i.e., on-line gaming, digital
communications, etc.)
A greater proportion of students reported engaging in
conventional collaboration compared to its digital counterpart (47% versus 35%) with the
notable exception of plagiarism (38% versus 51%). The study revealed students who
engage in conventional cheating are also likely engaging in some form of digital
cheating.
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Digital Technology and Academic Integrity
The growth in classroom and communication technology has created an increased
capacity for access to information (i.e., digital phones, Internet search engines, digital
cameras, calculators, etc.) As these technologies continue to evolve and become
commonplace in the classroom and at home, they become more familiar to students.
These technologies have many educational advantages, especially in a constructivist
classroom setting. They also may make the possibility of academic misconduct easier.
Increasingly, digital technologies are cited as being more commonplace in cheating
scenarios. While the problem of academic cheating has long since been characterized as
“epidemic” (Haines, Diekoff, LaBeff, & Clark, 1986, p. 775), recent reports in the
popular press (e.g., Thomas, 2001, McCarroll, 2001) have suggested that digital cheating
is on the rise, particularly Internet plagiarism. While anecdotal accounts abound and it
appears likely the use of technology may facilitate or amplify the cheating problem, little
empirical research has been conducted to substantiate the growing concern (for an
exception see McCabe, 2005).
In his study McCabe surveyed almost 50,000 undergraduates on more than 60
campuses since the fall of 2002 regarding their cheating behaviors (CAI Research, 2005).
Data from that study suggest that 70% of students admit to some form of cheating. 25%
admit to serious test cheating in the past year and 50% admitted to cheating on written
assignments. In a related longitudinal study conducted by the Center for Academic
Integrity data from nine medium to large universities suggests that disallowed
collaboration between students rose from 11% in 1963 to 49% in 1993 (CAI Research,
2002). Clearly cheating in a general sense is prevalent on the university campus. Both of
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these surveys pay only tacit attention to the realm of digital cheating, as it is a more
recent phenomenon.
The present University of Connecticut research seeks to help fill the gap in the
empirical work concerning digital cheating and to seek ways of addressing this behavior.
The primary purpose of the present pilot study is to determine what college students
report about their own digital cheating behaviors, characterize the level of technical
competency of the student sample, and determine relationships between characteristics of
the sample. It is our beliefs that: 1.) students who would cheat using traditional means
would use digital technologies to cheat if they had the technical competency to do so; 2.)
most students believe that digital technologies can be used to cheat; and 3.) affordances
(from an eco-psych perspective) created by technologies may foster cheating behaviors
in-situ for those students that might not otherwise cheat.
Method
Participants in the pilot study were undergraduates from two universities in the
northeastern United States. The sample of 116 students was: 59% male; 35% seniors,
33% juniors, 20% sophomores, and 12% freshmen. The final sample will include
approximately 1,000 undergraduates from several universities in the southeastern and
northeastern United States. The questionnaire was administered online. All students in
pilot study completed the questionnaire in May 2005 and were asked to confine their
responses to the 2004-2005 academic year. Data for the final sample will be collected in
December 2005.
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The measures for this pilot survey were obtained from a questionnaire fashioned
similarly to noted studies on academic misconduct in the traditional sense with added
emphasis (and particular questions) regarding digital forms of cheating. The
questionnaire was administered online and included original items as well as measures
adapted from McCabe’s (2001) Survey of Academic Integrity, Beck and Ajzen (1991)
moral responsibility scale, and Diekoff’s et al. (1996) neutralization scale.
Cheating behavior. The measure of cheating included original items as well as
items adapted from McCabe (2001). Students were asked to respond to a set of 12
academic behaviors by indicating on a five-point Likert-type scale how often they
engaged in each behavior during the current academic year (0= Never, 1= Once, 2=
Twice, 3= Three times, 4= Four or more times). The twelve items included six
traditional (non-digitally technological) forms of cheating and six analogous forms of
digital cheating. For example, students were asked to indicate how often they “copied a
few sentences from a book, magazine, or journal (not electronic or Web-based) without
footnoting them in a paper you submitted” as well as how often they “copied a few
sentences from the Internet without footnoting them”.
Judgment of responsibility. The measure of moral responsibility was adapted
from Beck and Ajzen (1991) and assessed the extent to which students judge cheating (or
refraining from doing so) as a personal moral responsibility. Specifically, students used a
five-point Likert-type scale (1= Strongly Disagree to 5= Strongly Agree) to indicate to
what extent they agreed with statement such as, “It would be morally wrong for me to
cheat on a test or exam” and “Cheating on tests or exams goes against my moral
principles” (4 items;  = .71).
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Neutralization of responsibility. The measure of neutralization was adapted from
Diekoff et al. (1996) and assessed the extent to students minimized personal
responsibility for engaging in academic cheating by displacing responsibility to social or
situational circumstances. Students used a five-point Likert-type scale (1= Strongly
disagree to 5= Strongly agree) to rate the extent to which they “should not be blamed for
cheating if…,” for example, “everyone else in the room seems to be cheating,” a “friend
asked him to help him/her cheat,” or “the instructor left the room” (items = 10;  = .93).
Acceptability of digital cheating. Students’ perception of the acceptability of
digital cheating was assessed with an original measure. Using a five-point Likert-type
scale (1= Strongly disagree to 5= Strongly agree), students indicated the what extent they
agreed with statement such as, “Computers and other digital technologies have made
cheating on homework, papers and exams more acceptable.” and “I don’t feel as bad or
guilty when I use digital technologies (such as computers, cell phones, and PDAs) to
cheat on homework, papers, and exams.” (6 items;  = .83).
Technological ability. Students’ technological ability was assessed with an
original scale. Using a five-point Likert-type scale (1= Never used to 5= Expert) to rate
their ability to use various digital technologies, such as PDA, or perform various tasks,
such as “install computer software” or “hack a computer or network” (7 items;  = .81).
Demographic information. Participants provided information about their gender
and year in school.
Finally, the questionnaire included one open ended question: “some students
believe that cheating is justified in certain situations and some students believe that it is
always wrong. What do you think?”
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Analysis Guidelines
Because students’ self-reported engagement in conventional and digital forms
were positively skewed (2.67 and 2.99, respectively), we dichotomized both of these
variables. For conventional cheating, students who reported that they never cheated
(30%) were coded with a “0” while those who reported engaging at least once in one of
the six behaviors (70%) were coded with a “1”. Similarly, students who reported that
they did not engage in any of the six digital forms of cheating (34%) were coded with a
“0” while those who reported doing so (66%) were coded with a “1”. Nonparametric
analytic techniques are deemed the most appropriate for data that is not normally
distributed. We conducted both parametric and nonparametric analyses. The results
were highly similar and in this report we report the results from the parametric analyses
because they are more easily interpreted.
Results
The results of our investigation helped us both better understand the
characteristics of the sample group, as well as, compare some of the key measures
regarding how people cheat. The sample was drawn from two universities in the
northeastern United States and subjects were chosen using a convenience sample.
The following results can be used to gauge the level of technical competency of
the respondents. 69.8% of students surveyed identified themselves as either expert or
advanced with respect to their use of technology. 13.8% felt as though they would be able
to successfully hack into a computer at the advanced or expert level. 78.2% felt they
would be able to adequately install computer software. 25% of respondents play
massively multi-player on-line role playing games (MMORPGS). 29.3% see themselves
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as video gamers. 96.6% of students think of themselves as fair and honest. 88% believe
that plagiarism is cheating. 74.8% believe that computers and other digital technologies
have made cheating easier. The students ranked the forms of technology that they felt
could be used to cheat. The rankings are shown in Table 1 and indicate that without
exception students felt strongly that all forms of technology listed could in someway be
used to cheat.
With respect to our question concerning the relative frequency of conventional
and digital cheating, results indicated significant differences on three of the six paired
behaviors. As seen in Table 2, a significantly greater proportion of students reported
engaging in conventional not permitted collaboration compared to its digital counterpart
(47% versus 35%, respectively). Similarly, more students reported using handwritten
“cheat sheets” to cheat on a test as opposed to using notes stored on a digital device, such
as a phone, PDA or calculator (19% versus 7%, respectively). Conversely, while only
38% of students reported conventional plagiarism (i.e., copying a few sentences from
written material without referencing the source), 51% reported engaging in digital
plagiarism (i.e., cutting and pasting a few sentences from the Internet without attribution).
These results suggest that plagiarism seems to be most impacted by student access
to digital technology than any other form of cheating. However, it is important to note
that there was not a significant difference in students’ overall self-reported rates of
conventional and digital cheating. This suggest that students who engage in some of
form of conventional cheating are also likely engaging in some form of digital cheating
as well. Although not discussed here, the nonparametric analyses presented in Table 3
offers support for this conjecture.
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As seen in Table 4, correlational analyses indicated a strong positive relation
between student engagement in conventional and digital cheating (r =.70). Gender and
grade level were not significant to either type of cheating. As hypothesized, students’
technological ability was not significantly related to conventional cheating but it was
significantly related to digital cheating. Finally, gender was negatively correlated with
technological ability and perceiving digital cheating as more acceptable; women reported
feeling less technologically able and were less likely to rate digital cheating as more
acceptable. Analysis of one specific group of students (those that play MMORPGS) there
was found to be a negative correlation between those students and cheating behaviors
Discussion
Generally, the results of the study do not agree with the consensus of the popular
media that access to technology by students has inflated the sheer numbers of incidents of
cheating as compared to non-technological forms of cheating. As seen by the data
presented, (with the notable exception of plagiarism) most students who cheat still
engage in traditional (non-technical) forms of cheating. Given that a large number of
respondents reported having an acceptable grasp of technology, it is not likely that this
result reflects any aversion to the use of technology in general. In fact, while most
students agree (see Table 1) that almost any kind of technology could be used to cheat
most do not report doing so.
The data do support the notion that students who would cheat anyway are
incorporating digital techniques into their “cheating repertoire” as circumstances become
aligned with that behavior. This is seen in the specific case of Internet based plagiarism.
Coincident with the stabilization of information resources on the Internet (access to
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papers, articles, moderated journals, etc.) we see a rise in the level of on-line plagiarism
as compared to traditional forms of plagiarism. This implies that the alignment of the
technical prowess of the student, the availability of appropriate technology, and
opportunities to capitalize on the cheating behavior increases the likeliness that a student
may choose to act.
One plausible way to explain the tendencies toward digital cheating is to consider
the situated nature of the act of cheating (Brown, Collins & Duguid, 1989). From this
perspective, cheating occurs in-the-moment, and is not subject to much if any reasoning,
planning, or reflection. From this ecological view (Young, 2004) cheating is embedded
in the momentary context and driven by the invitations given by the affordances that
contemporary technologies provide (Wilson, 2002). Students’ goals emerge in the
moment and the “online thinking” in conjunction with the cheating act is uninformed by
the “offline thinking” associated with reasoning and judgment. It is through the ecopsychological perspective that we are studying technologies’ role in providing the
affordances for cheating that certain students benefit from, based on the effectivities that
they have evolved based on their experience with technology. In this context, we
postulate that for most instances of cheating (e.g., test cheating, copying homework, etc),
technological affordances are not appropriately aligned with situations in which cheating
may take place or are being restricted (e.g., policies against the in-class use of a cell
phone) so as to minimize those opportunities. This could be used in devising ways of
reducing the number of reported cases of digital cheating.
Clearly, the survey data does indicate that when technological affordances are
available in a potential cheating situation, the cheating behavior flourishes. Existing
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research (McCabe, 2005) combined with data derived in this study indicate that the
specific case of Internet plagiarism is pervasive. This particular case merits further
study. It would appear digital plagiarism is clearly aligned with the eco-psychological
perspective in that it reflects “on-line” thinking, or thinking in the moment. The data also
suggests that even though most students agree that plagiarism is cheating and most
students see themselves as fair and honest, a greater percentage of students engage in this
form of cheating than in any other. This would suggest that either morale neutralization
strategies have been more effective on this cheating behavior than others, or that the act
of Internet plagiarism does not involve much morale reasoning at all.
It is clear that cheating in all its forms is a problem for educators. As the rise of
the use of technology continues (e.g., on-line classes and testing, distributed learning
environments, etc.) more opportunities to misuse technology in the academic setting will
arise. It is important to better understand the trends in digital cheating such that methods
can be devised to curtail or discourage those behaviors. Our research confirms that
cheating with technology is a phenomenon still in its infancy. Additional research efforts
are underway by this team to further explore the impact of digital cheating on teaching
and classroom methodologies, the effect of morale judgment on digital cheating, and
ways to effectively combat digital cheating in an ever increasing technology educational
environment.
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References
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Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the theory of planned
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Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of
learning. Educational Researcher, 18(1), 32-42.
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http://www.academicintegrity.org/cai_research.asp.
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(1996). College cheating: Ten years later. Research in Higher Education, 37(4),
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Kohlberg, L., & Candee, D. (1984). The relationship of moral judgment to moral action.
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http://www.academicintegrity.org/cai_research.asp
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http://www.csmonitor.com/2001/0828/p16s1-lekt.html
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delinquency. American Sociological Review, 22, 664-670.
Thomas, K. (2001). Net makes cheating as easy as ABC. USA Today, June 19, 2001.
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http://www.usatoday.com/news/nation/2001-03-20-cheat.htm.]
Wilson, M. (2002) Six views of embodied cognition.
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Wright, R. E. (1994). Logistic regression. In G. Grimm & P. R. Yarnold (Eds.), Reading
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Young, M. F. (2004). An ecological psychology of instructional design: Learning and
thinking by perceiving-acting systems. In D. H. Jonassen (Ed.), Handbook of
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Tables
Table 1
Student’s Ranking of Which Digital Technologies Can Be Used For Cheating
Technology
Percentage Agree
Digital Cellular Phone
93.2
Instant Messaging
PDAs
Laptops
Internet
E-mail
Calculators
Digital watches
Thumb drives
Digital recorders
Digital Cameras
MP3 players
GPS
Other
89.8
85.6
82.4
82.2
80.5
68.6
62.7
61.9
55.9
55.1
41.5
15.3
8.5
Table 2
Mean Proportions and Paired Sample t Statistics for Student Engagement in Conventional versus
Digital Forms of Cheating Behavior
Cheating Behavior
Variable
Copied homework
Unpermitted collaboration
t-Statistic
1.91
2.72*
Conventional
.23
.47
Digital
.16
.35
Plagiarized a few sentences
.38
.51
- 3.62*
Plagiarized a complete paper
.04
.03
1.00
Used “cheat sheets” or stored notes during a test or exam
.19
.07
3.45*
Copied from someone else during a test or exam
.14
.09
1.51
OVERALL
.70
.66
1.22
Note. Bonferroni adjustment was used to control for inflation in Type 1 error associated with
multiple comparisons: alpha = .05/6 = .008
* p < .008
\
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Table 3
Percentages of Student’ Self-Reported Engagement in Conventional and/or Digital Forms of Six
Cheating Behaviors
Variable
Copying homework
Unpermitted collaboration
Cheating Behavior
Conventional
Digital
11.2
4.3
18.1
6.0
Neither
72.4
47.4
Bo
12.
28.
Plagiarizing a few sentences
47.0
1.7
14.8
36.
Plagiarizing a complete paper
94.8
2.6
0.9
1.7
Using “cheat sheets” or stored notes during a test or exam
79.1
13.9
1.7
5.2
Copying from someone else during a test or exam
81.7
9.6
4.3
4.3
OVERALL
25.0
9.5
5.2
60.
Note. McNemar’s Test was used to determine statistical significance of differences in cell counts.
Underlined percentages represent those cases
in which the observed count significantly exceeded the expected count.
Table 4
Correlation Matrix, Means, and Standard Deviations
Variable
1. Conventional Cheating
2. Digital Cheating
1
–
.70***
2
3
- .09
- .12
–
4. Grade Level
- .08
- .03
.08
6. Judgment of Responsibility
5
6
–
3. Sex (female=1)
5. Technological Ability
4
–
.13
.19*
- .43***
.09
–
- .27**
- .27**
.20*
- .05
- .12
–
7. Neutralization of Responsibility
.28**
.35***
- .11
- .10
.12
-.43***
8. Digital Cheating More Acceptable
.26**
.39***
- .24**
- .10
.16
-.50***
M
0.70
0.66
0.41
2.91
3.46
4.01
SD
0.46
0.48
0.49
1.00
0.71
0.80
Note. N = 113-116.
* p < .05 ** p < .01
*** p < .001