Digital technology and academic dishonesty
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Running head: DIGITAL TECHNOLOGY AND ACADEMIC DISHONESTY
Does moral judgment go offline when students are online?
A comparative analysis of undergraduates’ beliefs and behaviors
related to conventional and digital cheating
Jason M. Stephens
Michael F. Young
Thomas Calabrese
University of Connecticut
Correspondence:
Jason M. Stephens, Ph.D.
Assistant Professor of Educational Psychology
University of Connecticut
249 Glenbrook Road, Unit 2064
Storrs, CT 06269-2064
jason.stephens@uconn.edu
The authors would like to thank the John Templeton Foundation for its encouragement and
financial support. We would also like to thank Dr. Donald McCabe for his important
contributions during the research, design and data collection phases of this project. The opinions
expressed in this paper are those of the authors and do not necessarily reflect the views of the
John Templeton Foundation or the Center for Academic Integrity.
Digital technology and academic dishonesty
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Abstract
The present study provides a comparative analysis of students’ self-reported beliefs and
behaviors related to six analogous pairs of conventional and digital forms of academic cheating.
Results from an online survey of undergraduates at two universities (N=1,305) suggest that
students use conventional means more often than digital means to copy homework, collaborate
when it is not permitted, and copy from others during an exam. However, engagement in digital
plagiarism (cutting and pasting from the Internet) has surpassed conventional plagiarism.
Students also reported using digital “cheat sheets” (i.e., notes stored in a digital device) to cheat
on tests more often than conventional “cheat sheets.” Overall, 32% of students reported no
cheating of any kind, 18.2% reported using only conventional methods, 4.2% reported using only
digital methods, and 45.6% reported using both conventional and digital methods to cheat.
“Digital only” cheaters were less likely than “conventional only” cheaters to report assignment
cheating, but the former was more likely than the latter to report engagement in plagiarism.
Students who cheated both conventionally and digitally were significantly different from the
other three groups in terms of their self-reported engagement in all three types of cheating
behavior. Students in this “both” group also had the lowest sense of moral responsibility to
refrain from cheating and the greatest tendency to neutralize that responsibility. The scientific
and educational implications of these findings are discussed in this study.
Keywords: Academic Dishonesty, Technology, Moral Judgment, College Students.
Digital technology and academic dishonesty
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Digital technologies such as cell phones, personal digital assistants (PDAs), computers
and the Internet are powerful tools that have greatly facilitated modern communication,
commerce and education. At their best they help us do our work more efficiently, accurately,
and even creatively. However, these technologies can also make it easier to steal and cheat, or to
otherwise deceive and defraud others. While the problem of academic cheating has long since
been characterized as an “epidemic” (Haines, Diekhoff, LaBeff, & Clark, 1986, p. 775), recent
reports in the popular press (e.g., Bushweller, 1999; McCarroll, 2001; Sterba & Simonson, 2004)
have suggested that digital forms of academic cheating are on the rise. Although anecdotal
accounts of digital cheating abound, and it appears likely the use of technology may facilitate or
amplify the problem of cheating, very little empirical research has been conducted to substantiate
the growing concern (for exceptions, see Lester & Diekhoff, 2002; McCabe, 2005).
The present study seeks to help fill the gap in the empirical work concerning digital
cheating. In addition to providing a comparative analysis of college students’ engagement in six
analogous forms of conventional and digital cheating behavior, the present study investigates the
demographic, psychological, and social factors associated with conventional and digital cheating.
As the subtitle of this paper suggests, we are not only interested in students’ behavior but also
their normative judgments related to cheating. Specifically, we explore students’ beliefs about
the relative seriousness of conventional and digital cheating, the extent to which they feel
morally obligated not to cheat, as well as the extent to which they tend to displace or diffuse
responsibility for cheating, and their perceptions of their peers’ attitudes and behaviors related to
cheating. Given our special interest in digital cheating, we also investigate the relations between
students’ cheating and their technological ability and concerns about being monitored while
online.
Digital technology and academic dishonesty
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Cheating Among Undergraduates
As noted above, the problem of academic cheating has long since been characterized as
an “epidemic” (Haines, Diekhoff et al., 1986, p. 775). The largest and most comprehensive
studies of academic dishonesty among undergraduates have been conducted by Donald McCabe
and his colleagues (McCabe, 1992; McCabe & Bowers, 1994, 1996; McCabe & Pavela, 2000;
McCabe & L. K. Trevino, 1993; McCabe & Trevino, 1996, 1997; McCabe, Trevino, &
Butterfield, 1999). McCabe’s most recent report (2005), based on data collected for the Center
for Academic Integrity Assessment Project encompassing nearly 50,000 undergraduates at more
than 60 institutions, revealed that 70% of students were engaging in some cheating and almost
25% admitted to cheating on a test or exam. Other researchers have reported similar statistics
(Davis, Grover, Becker, & McGregor, 1992; Diekoff et al., 1996; Haines, Diekhoff et al., 1986;
Newstead, Franklyn-Stokes, & Armstead, 1996). Several of the studies suggest that the problem
of academic cheating has been growing over the past two or three decades (Diekoff et al., 1996;
McCabe, 2005; McCabe & Bowers, 1994; McCabe & Trevino, 1996).
McCabe’s (2005) recent report also highlights the growing problem of Internet
plagiarism. While only 10% of students admitted to “cut and paste” plagiarism in 1999, nearly
40% admitted doing so in the CAI Assessment Project surveys (collected over a three-year
period, 2002-2004). In addition, these recent surveys revealed that 77% of students did not
believe such plagiarism was very serious. The only published empirical study (Lester &
Diekhoff, 2002) offering a comparison of conventional versus digital plagiarism indicated that
among those who admitted to cheating (68% of the total sample), only 12% reported using the
Internet to do so. Moreover, most of the students who reported using the Internet to cheat also
Digital technology and academic dishonesty
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reported using conventional means; “only four students (1.3%) indicated that their cheating was
exclusively Internet-based” (p. 909).
In exploring individual differences among conventional, “traditional” cheaters and
Internet cheaters, Lester and Diekoff (2002) found significant gender and attitudinal differences.
Specifically, a majority of traditional cheaters were women while a majority of Internet cheaters
were men, and the former were less likely than the latter to resent (and more likely to ignore) the
cheating of others. Finally, traditional cheaters were less likely than digital cheaters to justify or
neutralize responsibility for digital cheating.
Moral Judgment, Neutralization, and Cheating
The difference between traditional and digital cheaters in the tendency to justify cheating
found by Lester and Diekoff (2002) reflects a broader difference between non-cheaters and
cheaters. Previous studies with undergraduates (Haines, Diekoff, LaBeff, & Clark, 1986;
LaBeff, Clark, Haines, & Diekoff, 1990; Lester & Diekhoff, 2002) have demonstrated a strong
positive association between cheating and the tendency to justify or “neutralize” responsibility of
cheating. These neutralization techniques or disengagement may, in part, explain why so many
students who report that they believe cheating is wrong also report doing it anyway (Anderman,
Griesinger, & Westerfield, 1998; Davis et al., 1992; Jordan, 2001).
It seems evident that while feeling a moral obligation to refrain from cheating can
mitigate the extent of cheating (Beck & Ajzen, 1991), this sense of obligation can be deactivated
or “neutralized” through various mechanisms or techniques, such as minimizing consequences
(“it’s no big deal”), euphemistic labeling ( “it’s not really cheating”), displacing responsibility
(“it’s my teachers’ fault”) and diffusing responsibility (“everyone else was doing it”). These are
Digital technology and academic dishonesty
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just a few of the many disengagement mechanisms (Bandura, 1986) or neutralization techniques
(Sykes & Matza, 1957) that individuals might employ to avoid or reduce self-recrimination when
they have behaved criminally or immorally. Theoretically, these mechanisms can also be seen as
the antithesis of Kohlberg and Candee’s (1984) responsibility judgment. Rather than affirming
the will and activating “the self’s accountability to perform the right action” (Kohlberg &
Candee, 1984, p. 57), they obscure or even negate one’s personal agency by attributing
responsibility for one’s conduct to others or to situational contingencies.
The Internet may exacerbate the tendency to disengage or neutralize personal moral
responsibility. This so-called “online disinhibition effect” (Suler, 2004) may be responsible for
other forms of unethical behavior that digital technologies seem to be facilitating. In a
nationwide survey of undergraduates, for example, the Business Software Alliance (2003) found
that while 69% of students downloaded music from the Internet, only 2% consistently paid for
that music; 75% said they never pay for it. Similarly, while 26% of students said they have
downloaded movies, only 1% consistently paid for them and 84% said they never pay. Not
surprisingly, 75% students said that it is “OK” to pirate music and movies to save money. While
this study (or any other that we could find) did not connect this kind of piracy with plagiarism, it
seems likely that the two are correlated and that the tendency to neutralize responsibility
mediates the relationship.
Peer Norms and Academic Cheating
Human thought and action do not happen in a vacuum. There is a long history of
empirical work in psychology, particularly in social psychology, demonstrating the powerful
effect peers can have on each other’s beliefs and behaviors (Asch, 1951; Festinger, 1954;
Digital technology and academic dishonesty
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Newcomb, 1943; Sherif, 1936). Several more recent studies have investigated the relations of
peer-related variables to academic dishonesty among college students (e.g., Bowers, 1964;
Graham, Monday, O'Brien, & Steffen, 1994; Jordan, 2001; Lanza-Kaduce & Klug, 1986;
McCabe & Trevino, 1997; McCabe & L. K. Trevino, 1993; McCabe, Trevino, & Butterfield,
2001).
In their multi-campus investigation of individual and contextual influences related to
academic dishonesty, McCabe and Trevino (1997) found peer disapproval of cheating and peer
cheating behavior to be the two strongest predictors of self-reported cheating: Students who
perceived that their peers disapproved of academic dishonesty were less likely to cheat, while
those who perceived higher levels of cheating among their peers were more likely to report
cheating. Similarly, Jordan (2001) found a highly significant correlation between college
students’ perceived social norms and their self-reported cheating. Students who cheated not only
reported higher estimates of the percentage of students who cheated at their college than noncheaters (31.2% vs. 20.6%, respectively), they also reported significantly higher rates of having
seen someone else cheat (70.8% vs. 40.5%, respectively).
The Present Investigation
The present study extends the work of Lester and Diekhoff (2002) by offering a
comparative analysis of not only traditional versus Internet plagiarism, but also conventional
versus digital cheating on assignments and exams. Consistent with the work of McCabe (2005)
we expected students to report using the Internet to plagiarize as often as conventional means.
However, we also expected to find students still using conventional means more than digital
means to cheat on assignments and tests. Like Lester and Diekhoff (2002), we also expected to
Digital technology and academic dishonesty
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find relatively few students who use only the Internet or other digital technologies to cheat; we
expected the majority of students to report using both conventional methods and digital
technologies to cheat.
The present study also explores students’ beliefs about the relative seriousness of three
types of conventional and digital cheating. We expected students’ beliefs about seriousness of
cheating to reflect their engagement in these behaviors; conventional cheaters will rate forms of
conventional cheating as less serious than forms of digital cheating and that digital cheaters will
exhibit the opposite pattern. We did not, however, expect to find significant differences between
conventional and digital cheaters in terms of their moral responsibility to refrain from nor in their
tendency to neutralize that responsibility. Unlike Lester and Deikhoff’s (2002) measure of the
latter, the measures of moral responsibility and neutralization used in this study do not
distinguish between conventional and digital cheating; thus, we did not expect to find any
differences between conventional and digital cheaters on these measures. We do, however,
expect to find significant differences between students who do not cheat and those that do
(conventional and/or digitally): the former were hypothesized to report higher levels of moral
responsibility and lower levels of neutralization.
In addition to the foregoing psychological process variables, the present study
investigated the relations between perceptions of peer norms and self-reported cheating.
Specifically, we expected to find significant differences between conventional and digital
cheaters with respect to their perceptions of peer acceptability of cheating; we expected the latter
to perceive higher levels of acceptability. We did not expect these two groups to differ with
respect to perceptions of peer cheating behavior (as with our moral cognitive measures, this
measure did not distinguish between conventional and digital cheating). We did expect to find
Digital technology and academic dishonesty
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significant differences between non-cheaters and cheaters (conventional and/or digital) on both
measures of peer norms: the former were hypothesized to report lower levels of peer
acceptability of digital cheating and peer engagement in cheating behavior.
Finally, as noted in the introduction, digital technologies have greatly facilitated modern
communication, commerce and education. When used wisely, they help us do our work more
efficiently as well as creatively. However, these technologies may also make it easier to
perpetrate a wide range of dishonest behaviors. In the present study, we assessed both students’
perceived ability to use various digital technologies as well as their concerns about being
monitored while online. We expected to find significant differences among conventional and
digital cheaters on both of these measures. Specifically, we hypothesized that digital cheaters
would possess both more technological ability as well as more concern about being monitored.
Method
Participants and Procedures
Participants in the study were undergraduates from two universities in the United States.
School 1 was a private university in the northeastern United States with an enrollment of just
approximately 3,900 undergraduate students. School 2 was a public land-grant university in the
southern United States with an enrollment of approximately 12,000 undergraduates. Both
institutions grant bachelor, masters, and doctoral degrees in numerous disciplines. Neither
institution has an honor code nor a school-wide program focused on developing students’
fluency with digital technologies.
Participation was solicited via email in November, 2005. All undergraduates at School 1
and a random sample of 2,500 at School 2 were sent an email directing them to an anonymous
online questionnaire about academic integrity. Once at the Web site, participants were again
Digital technology and academic dishonesty 10
assured anonymity and asked to confine their responses to courses they had taken within the past
year. The final combined sample of 1,305 students was 58.6% female, 40.3% seniors, 25.3%
juniors, 15.2% sophomores, and 19.2% freshmen.
Measures
The questionnaire 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 et al.’s (1996) neutralization scale.
Cheating measures. Students’ cheating beliefs and behaviors were assessed with 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 three-point Likert-type scale how often they engaged
in each behavior during the past academic year (0= Never, 1= Once, 2= More than once).
Students were also asked to indicate how serious they believed each behavior was (1= Not
cheating, 2= Trivial cheating, 3= Moderate cheating, 4= Serious cheating). The 12 items
included six conventional forms of cheating and six analogous forms of digital cheating. These
six pairs of behaviors – including two forms each of assignment cheating, plagiarism, and test
cheating – are described below in Table 1.
------------------------Insert Table 1 here
-------------------------Moral cognition. Two aspects of moral cognition were measured: a judgment of personal
moral responsibility to refrain from cheating and a tendency to neutralize responsibility for
cheating by displacing or diffusing blame. The first aspect, moral responsibility, was adapted
from Beck and Ajzen (1991). Students used a five-point Likert-type scale (1= Strongly Disagree
Digital technology and academic dishonesty 11
to 5= Strongly Agree) to indicate to what extent they agreed with statements 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” (3 items;  = .76). The measure of students’ tendency to neutralize
responsibility was adapted from Diekoff et al. (1996). Students used a five-point Likert-type
scale (1= Strongly disagree to 5= Strongly agree) to rate the extent to which “Students 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” (5 items;  = .84).
Peer norms. Two aspects of peer norms – attitudes and behaviors related to academic
cheating – were assessed. Peer 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 to what extent they agreed with statements 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” (3 items;  = .75). The measure of peer
cheating behavior was adapted from McCabe (2001). Using a five-point Likert-type scale (1=
Never to 5= Very often), students indicated how frequently they thought students at their school
engaged in unpermittted collaboration, plagiarism, and test cheating (3 items;  = .75).
Technology measures. Students’ technological ability was assessed with an original
scale. Students used a five-point Likert-type scale (1= Never used to 5= Expert) to rate their
ability to use various digital technologies, such as PDAs, or to perform various tasks, such as
“install computer software” or “hack a computer or network” (7 items;  = .86). Students’ level
of concern or suspicion that their Internet communications were being monitored was assessed
with original items. Using a five-point Likert-type scale (1= Strongly disagree to 5= Strongly
Digital technology and academic dishonesty 12
agree), students indicated to what extent they agreed with statements such as, “I am not
concerned about anonymity when online,” (negatively-keyed) and “I am concerned that my
email is monitored” (3 items;  = .62).
Demographic information. Participants provided information about their sex (female=0;
male= 1) and year in college (i.e., Freshmen, Sophomore, Junior, or Senior).
Results
Analysis Guidelines
Because students’ self-reported engagement in all of the conventional and digital
cheating behaviors were positively skewed, we dichotomized each of the 12 items. Students who
reported that they did not engage in the behavior were coded with a “0” while those who
reported engaging at least once in the behavior were coded with a “1”. Nonparametric analytic
techniques are deemed the most appropriate for data that is not normally distributed.
Accordingly, we used Chi-square analyses to assess differences among the six analogous pairs of
cheating behavior (see Table 2).
We also used self-reports on the 12 individual cheating behaviors to create four unique
subgroups of students: 1) “non-cheaters” – students that reported that they did not engage in any
of 12 behaviors; 2) “conventional only” cheaters – students that reported engaging in one or
more of the six conventional behaviors but none of the six digital behaviors; 3) “digital only”
cheaters – students that reported engaging in one or more of the six digital behaviors but none of
the six conventional behaviors; and 4) “conventional-digital” cheaters – students that reported
engaging in one or more of the six digital behaviors and in one or more of the six conventional
behaviors.
Digital technology and academic dishonesty 13
We then used cross-tabulation (chi-square) analyses to assess differences in the three
types of academic dishonesty (i.e., assignment cheating, plagiarism, and test cheating) among the
three subgroups of students who cheated (see Table 5). Finally, multinomial logistic regression
analyses (using non-cheaters as the common reference group) were used to examine the role of
demographic, psychological, and perceived contextual variables in predicting group membership
for “conventional only,” “digital only” and “conventional-digital” cheating (see Table 9).
Self-Reports of Conventional and Digital Cheating Behavior
With respect to our first question concerning the relative frequency of conventional and
digital cheating, results indicate that overall a significantly greater percentage of students
reported conventional cheating than did digital (63.8% versus 49.8%, respectively; 2(1, N = 1305 ) =
112.20, p < .001). As seen in Table 2, this overall effect was largely caused by substantial
differences in the two forms of assignment cheating; over three times as many students reported
copying homework in a conventional way (i.e., in person) than digitally (i.e., over the Internet or
via email) and nearly twice as many reported engaging in unpermitted collaboration face-to-face
than doing so online.
Contrary to our hypothesis, there were no significant differences between engagement in
conventional and digital forms of plagiarism, although a slightly higher percentage of students
reported cutting and pasting a few sentences from the Internet. Finally, a significantly greater
proportion of students reported using digital “cheat sheets” (notes stored in a digital device, such
as a phone, PDA or calculator) than handwritten “cheat sheets” to cheat (19% versus 7%,
respectively). Conversely, a significantly greater percentage of students reported copying from
another student’s paper during an exam as opposed to using a digital device (cell phone or text
messaging) to get unpermitted help during an exam.
Digital technology and academic dishonesty 14
------------------------Insert Table 2 here
-------------------------Contrary to our hypotheses, students did not, overall, rate digital forms of academic dishonesty
as less serious than conventional forms of dishonesty (3.11 vs. 3.12, respectively; t(1285) = -0.99,
p = ns). Means, standard deviations, and paired-sample t-statistics as well as effect sizes
(Cohen’s d) of students’ seriousness ratings for each of six analogous pairs of cheating behaviors
are presented in Table 3. Only one statistically significant difference emerged from these
analyses: Students rated the use of unpermitted digital notes (such as those stored in a PDA or
calculator) during a test or exam as less serious than the use of conventional “cheat sheets” (t(1292)
= -5.94, p < .008; d = -0.11). Given the relative ease of Internet plagiarism and the more tenuous
or uncertain sense of intellectual ownership/copyright that students seem to attribute to Webbased resources, we expected students to rate “cut and paste” plagiarism as less seriousness than
conventional plagiarism. They did not (2.85 vs. 2.83, respectively; t(1282) = 1.79, p = ns ; d = 0.02).
------------------------Insert Table 3 here
-------------------------Table 4 offers a further illustration of the behavioral differences depicted in Table 2 and
highlights the fact that, with the exception of the two test cheating behaviors, the vast majority of
students who use digital technologies to cheat also use conventional methods. This table also
shows a great disparity in the use of conventional versus digital to copy homework or to engage
in unpermitted collaboration – relatively few students use digital technologies exclusively to do
either. Conversely, relatively few students use conventional means exclusively to plagiarize –
most students who plagiarize report using both methods. Finally, this table reveals that overall
Digital technology and academic dishonesty 15
32% of students reported no cheating of any kind, 18.2% reported using only conventional
methods, 4.2% reported using only digital methods, and 45.6% reported using both conventional
and digital methods to cheat.
------------------------Insert Table 4 here
--------------------------
Table 5 shows results from cross-tabulation analyses (with chi-square tests) examining
differences between the three subgroups of students who reported cheating by three types of
cheating: assignment cheating, plagiarism, and test cheating. Significant differences emerged
between these cheating groups on all three types of cheating. Consistent with the pattern of
results in Table 4, “digital only” cheaters were significantly less likely than the other two groups
to report assignment cheating and significantly more likely than “conventional only” cheaters to
report engagement in plagiarism. All three groups were significantly different from one another
with respect to test cheating: “conventional only” being the least likely and “conventionaldigital” being the most likely to engage in test cheating. The following section of results
explains how students in these three subgroups and those who did not cheat differ with respect to
several sets of outcomes.
------------------------Insert Table 5 here
-------------------------Differences Between The Four Cheating Groups
These data were analyzed with a multivariate analysis of variance (MANOVA) design
with cheating group (non-cheater, conventional only, digital only, conventional-digital) and
school (private, public) as the two between-subjects factors. As seen in Table 6, there were
significant effects for cheating groups on all but one (technology attributes) of the sets of
Digital technology and academic dishonesty 16
outcomes. Conversely, there was only one significant effect for school (peer norms). No
cheating group by school interactions emerged for any of the outcomes.
------------------------Insert Table 6 here
-------------------------The univariate statistics presented in Table 7 encompass all of the main effects for the
cheating group only, and are described below by construct set. An examination of the univariate
statistics for the main effect for school revealed significantly higher perceptions of peer cheating
behavior at School 1 (the smaller private university, F(1, 1285) = 17.94, p < . 001, = .014);
perceptions of peer acceptability of cheating did not differ significantly between these two
schools (F(1, 1285) = 0.01, p =.912, = .000). Finally, analyses of demographic variables
revealed an over-representation of conventional only and an under-representation of
conventional-digital cheaters at the larger public institution (2(3, N = 1305) = 18.86, p < .001). In
other words, there were disproportionately fewer conventional only cheaters and
disproportionately more conventional-digital cheaters at the smaller private university. There
were not any significant differences in the distribution of gender (2(3, N = 1292) = 4.75, p = ns) nor
in the year in college (F(3,1264) = 2.69, p = ns).
------------------------Insert Table 7 here
-------------------------Cheating beliefs. Significant differences emerged on students’ beliefs about the
seriousness of both conventional and digital cheating. Once again, the most noteworthy
comparison concerns the “conventional only” and “digital only” subgroups. Reflecting their
differences in self-reported behavior, “conventional only” cheaters rated assignment cheating as
less serious than did “digital only” cheaters (2.45 versus 2.61, respectively), while “digital only”
Digital technology and academic dishonesty 17
cheaters rated plagiarism as less serious than “conventional only” cheaters (3.22 versus 3.43,
respectively). The latter difference achieved statistical significance but the former difference did
not. Students in these two groups did not differ significantly on any of the other four behaviors.
Also notable were the significant differences between “non-cheaters” and the “conventionaldigital” group. Students who did not report cheating rated both assignment cheating and
plagiarism as more serious than students who reported using both conventional and digital means
to cheat. The effect size for assignment cheating was the largest (.12) followed by plagiarism
(.04) and then test cheating (.02).
Moral cognition. Significant differences between groups emerged for both students’
sense of moral responsibility to refrain from cheating and their tendency to neutralize that
responsibility. Because neither of the measures distinguished between conventional and digital
cheating, we did not expect to find any differences between “conventional only” and “digital
only” cheaters on these measures. Contrary to this expectation, “digital only” cheaters reported
significantly higher ratings of moral responsibility than did “conventional only” cheaters (4.73
versus 4.50, respectively). They did not, however, differ on their tendency to neutralize
responsibility for cheating. Students in the “both” group had significantly lower ratings of moral
responsibility than all three of the other groups and significantly higher neutralization scores than
the “neither” group. The effect sizes for both of these measures were quite small (.04 for moral
responsibility and .05 for neutralization).
Perceptions of peer norms. Significant differences between groups emerged for students’
perceptions of peer acceptability of digital cheating and peer cheating behavior. Because the
former measure specifically focused on digital cheating, we did expect to find significant
differences between “conventional only” and “digital only” cheaters on this measure. However,
Digital technology and academic dishonesty 18
we did not. Nor did students in these two groups differ significantly with respect to their
perception of peer cheating behavior. Consistent with the pattern of the foregoing findings,
students in the “both” group differed significantly from the students in the “neither” group on
both measures. The former also rated peer acceptability of digital cheating significantly higher
than “conventional only” students, and they also reported perceiving significantly higher levels
of peer cheating behavior than did “digital only” students. The effect sizes for both of these
measures were quite small but larger for peer acceptability (.07) than for peer cheating behavior
(.03).
Relations Between Measures
As seen in Table 8, correlational analyses indicated a strong positive relation between
student engagement in conventional cheating and digital cheating (r =.58). School and grade
level had very small (though statistically significant) correlations with conventional and digital
cheating, respectively. Contrary to expectations, neither students’ technological ability nor their
concerns about being monitored online were correlated with either conventional or digital
cheating. As expected, students’ beliefs about the seriousness of cheating and their judgments of
moral responsibility (to refrain from cheating) were both negatively associated with both forms
of cheating. Conversely, and also as expected, students’ tendency to neutralize personal
responsibility for cheating and their perception of peers’ acceptability of digital cheating and
cheating behavior were all positively correlated with both forms of cheating.
------------------------Insert Table 8 here
--------------------------
Digital technology and academic dishonesty 19
Predictors of Conventional and Digital Cheating
Multinomial logistic regression analyses were used to predict group membership for
“conventional only,” “digital only” and “conventional-digital” cheating using non-cheaters as the
common reference group. Table 9 contains the regression coefficients, standard errors, and the
odds ratios for the final model. The odds ratio represents the change in the odds of group
membership (“conventional only” versus “non-cheater”; “digital only” versus “non-cheater”;
“conventional-digital” versus “non-cheater”) given a one-unit increase in the predictor variable.
For example, the odds of being a “conventional only” cheater (versus a “non-cheater”) are .55
times lower for a student whose beliefs about the seriousness of cheating are 3.0 as opposed to
2.0 (after accounting for other variables in the equation). Or, put another way, for every unit
increase in cheating beliefs, a student becomes half as likely to become a “conventional only”
cheater. As seen in Table 8, cheating beliefs was the only predictor to achieve significance for
predicting membership for all three groups. In each case, students were approximately half as
likely to be a cheater for every unit increase in their beliefs about the seriousness of cheating.
Beyond this commonality, group membership in the three subgroups was predicted by a
different configuration of variables. “Conventional only” cheating was more likely: 1) at the
large public institution, 2) among older students, and 3) as neutralization of responsibility
increases. “Conventional only” cheating was less likely as concerns about being monitored and
beliefs about the seriousness of cheating increased. “Digital only” cheating was more likely as
moral responsibility and perceptions of peers’ acceptability of digital cheating increased, and less
likely as beliefs about the seriousness of cheating increased. Finally, “conventional-digital”
cheating was more likely among older students and as perceptions of peers’ acceptability of
Digital technology and academic dishonesty 20
digital cheating and peer cheating behavior increased. It was less likely at a large public
institution and as concerns about being monitored increased.
------------------------Insert Table 9 here
--------------------------
Discussion
Results from the present study suggest that students use conventional means more often
than digital means to copy homework, to collaborate when it is not permitted, and to copy from
others during an exam. However, a slightly greater percentage of students reported having
engaged in digital plagiarism (cutting and pasting from the Internet without attribution) than
having engaged in conventional plagiarism. Students also reported using digital “cheat sheets”
(i.e., notes stored in digital device) to cheat on tests more often than using conventional “cheat
sheets.” Overall, 32% of students reported no cheating of any kind, 18.2% reported using only
conventional methods, 4.2% reported using only digital methods, and 45.6% reported using both
conventional and digital methods to cheat. These findings suggest that most students who cheat
use both conventional and digital means to do so, and that very few students use digital means
exclusively. These findings support and extend the work of Lester and Diekoff (2002) who also
found very few “digital only” cheaters, but had limited their scope to plagiarism.
Beyond exploring behavioral differences in conventional and digital cheating, the present
study was designed to investigate students’ beliefs, moral judgments, and peer perceptions
related to cheating. We were especially interested in determining the extent to which students
regarded digital cheating as less serious than conventional cheating. The findings did not
support our hypothesis that students viewed digital cheating as less serious than conventional
cheating. These findings did, however, illustrate that a student’s beliefs about the seriousness of
Digital technology and academic dishonesty 21
cheating is a strong negative predictor of cheating behavior, conventional and digital. Results
from multinomial logistic regression analyses also indicated that perceptions of peer
acceptability of digital cheating were a strong positive predictor of digital cheating.
Limitations
The greatest concern or limitation of any study that asks individuals to report their
engagement in deviant or socially sanctioned behavior concerns the veracity of those reports.
This concern is exacerbated when this reporting takes place online, where fear of detection may
be greater, and it likely accounts for both the relatively low response (20.2%) as well as the
lower incidence of self-reported cheating behavior found in this study compared to others (cf.
McCabe, 2005). The low response rate of this study may also be a product of a broader trend.
According to Sheenan (2001), response rates of electronic surveys have steadily dropped over
the period from 1986 through 2000 (61.5% vs. 24%, respectively). Sheenan suggests that the
novelty of online surveys has worn off and that researchers use incentives and offer multiple
modes of participation (e.g., mailing paper surveys with the URL of a Web site for those who
prefer to complete survey online) in order to increase response rates. The proliferation of online
surveying (not to mention spam and filtering software to detect and redirect unsolicited, mass
email) since 2000 has only increased the challenge of soliciting participation. The present study
did not use incentives or offer students multiple modes of participating. Future researchers
should consider doing so.
The limited range of behaviors is perhaps another limitation of the present study. We
only asked students about 12 behaviors – six matched pairs of conventional and digital cheating.
There are, of course, many more forms of cheating, particularly digital cheating. Consequently,
Digital technology and academic dishonesty 22
we cannot definitively conclude that conventional cheating is still more prevalent overall. Future
research should broaden the range of behaviors investigated.
In terms of sampling design, all participants in the present study were asked to rate the
seriousness of both conventional and digital behaviors. We had hypothesized that students
would rate several of the digital cheating behaviors as less serious, particularly Internet
plagiarism. While this hypothesis was supported when we compared subgroups of students
based on cheating method (i.e., conventional only vs. digital only), the effect was quite small.
Overall, students to did rate digital forms of academic dishonesty as less serious than their
conventional counterparts. This may be a result of what Dillman (2000) called the “norm of
even-handedness,” whereby students adjust responses to remain consistent with their responses
on similar previous items. Future research aimed at comparing students’ beliefs about the
seriousness of conventional and digital cheating should consider employing a between-subject
design with random assignment to one of two forms: one asking about conventional cheating
beliefs or one asking about beliefs related to digital cheating items, not both.
Finally, most of the analyses explained relatively modest amounts of variance. Future
investigations in this area should include of a wider range of independent variables to provide a
better sense of the factors that are associated with conventional and digital cheating, and perhaps
the differences between them. For example, while the present study measured students’
perceptions of their technological competence and their concerns about monitored while online,
it did not examine the nature or extent of students’ use of various technologies. Nor did we
examine the extent to which the two schools required or promoted technology usage. Both of
these factors may influence the amount of digital cheating students engage in. Future research
should consider the inclusion of these or similar variables.
Digital technology and academic dishonesty 23
Educational Implications and Conclusion
The present study provides some insights for educators and administrators who are
interested in better understanding and ultimately ameliorating the problem of academic
dishonesty, digital and conventional.
First and foremost, results from this study suggest that at present the extent and nature of
the role of digital technologies in academic dishonesty may not be as severe as the popular press
would lead one to believe. Very few students (only 4.2% of our sample) exclusively used digital
technologies to cheat; most students use both conventional and digital means. This suggest that
the Internet and other digital tools are conduits and not causes of academic dishonesty (for more
on this distinction, see McCabe & Stephens, 2006). That said, it is important to note that even if
digital technologies have not caused cheating nor created a new generation of cheaters, they may
still be exacerbating the problem. The “freedom” of Internet, in particular, seems to further
obfuscate already abstract concepts such as intellectual property and copyright.
Over the past several years, many schools seemed to have focused on technological
solutions, such as turnitin.com, to combat Internet plagiarism. While these text-matching
detectors may deter some students from plagiarizing, they constitute a reactive approach –
catching students after they have fallen. From a developmental standpoint, this is not the optimal
situation – punishing students for doing the wrong thing rather than helping them do the right
thing. Accordingly, we suggest a combination of both pedagogical and cultural approaches that
aim to prevent plagiarism and other forms of academic dishonesty (conventional and digital)
from occurring in the first place.
Digital technology and academic dishonesty 24
With respect to pedagogy, there are many strategies that faculty can employ to decrease
the probability of student plagiarism. Creating original and interesting writing assignments is a
good start but requiring a series of process steps is probably more important. For example,
breaking down writing project into a series of small, progressive tasks – selecting a topic,
creating an outline, generating a bibliography, producing a first draft, etc. – helps prevent the
kind of late-night, last-minute plagiarism that the Internet seems to facilitate. In terms of culture,
schools at all levels (elementary to graduate) must to do more to promote students’
understanding and appreciation of core academic (and moral) values, such as honesty, trust,
fairness, respect, and responsibility. Educators and institutions interested in doing so should
consult with organizations such as The Center for Academic Integrity for effective strategies and
helpful advice.
In sum, academic dishonesty is a widespread problem. While digital technologies are not
causing the problem, they are contributing to it, and likely increasingly so. It’s clear that
availability and sophistication of digital technologies is only going to grow overtime, and the
present findings provide a baseline to track what may a shift in the nature of cheating (away from
conventional means toward digital ones). While using digital technologies and programs such as
Turnitin.com may deter some would-be cheaters, faculty and institutions wishing to help
ameliorate the problem a more proactive and developmental manner should focus their efforts on
creating classroom communities and campus cultures that clearly communicate the meaning and
importance of academic integrity. Fostering students’ personal and collective responsibility for
acting fairly, honestly, and responsibly in their academic endeavors may be one of the best ways
to prevent their moral judgment from going offline when they are online.
Digital technology and academic dishonesty 25
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Digital technology and academic dishonesty 29
Table 1
Six Matched Pairs of Conventional and Digital Forms of Cheating Behavior
Cheating Behavior
Variable
Conventional
Copying (by hand or in person) another
student’s homework.
Digital
Copying (using digital means such as
Instant Messaging or email) another
student’s homework.
Unpermitted collaboration
Working on an assignment with others (in
person) when the instructor asked for
individual work.
Working on an assignment with others (via
email or Instant Messaging) when the
instructor asked for individual work.
Plagiarized a few sentences
Paraphrasing or copying a few sentences
from a book, magazine, or journal (not
electronic or Web-based) without footnoting
them in a paper you submitted
Paraphrasing or copying a few sentences
from an electronic source (e.g., the Internet)
without footnoting them in a paper you
submitted
Plagiarized a complete paper
Turning in a paper from a “paper mill” (a
paper written and previously submitted by
another student) and claiming it as your
own work.
Submitting a paper you purchased or
obtained from a Web site (such as
www.schoolsucks.com) and claimed it as
your own work.
Used unpermitted notes during a test or exam
Using unpermitted handwritten crib notes
(or cheat sheets) during a test or exam.
Copied from someone else during a test or exam
Copied from another student's paper during
a test or exam with his or her knowledge.
Using electronic crib notes (stored in a
PDA, phone or calculator) to cheat on a test
or exam.
Using digital technology (such as text
messaging) to get unpermitted help from
someone during a test or exam.
Copied homework
Digital technology and academic dishonesty 30
Table 2
Mean Percentages and Paired Sample Statistics for Student Engagement in Conventional versus Digital
Forms of Cheating Behavior
Cheating Behavior
Variable
2
Conventional
Digital
Copied homework
35.8
11.3
283.80***
Unpermitted collaboration
42.8
22.7
190.57***
Plagiarized a few sentences
30.7
32.2
1.87
Plagiarized a complete paper
1.9
0.5
--
Used unpermitted notes during a test or exam
8.3
10.8
6.16**
Copied from someone else during a test or exam
8.6
1.8
66.40***
63.8
49.8
112.20***
OVERALL
Note.
* p ≤ .05
** p ≤ .01
*** p ≤ .001
(1, N=1305)
Digital technology and academic dishonesty 31
Table 3
Means (Standard Deviations) and Paired Sample t and Cohen’s d Statistics for Students’ Beliefs about the Seriousness of
Conventional versus Digital Forms of Cheating Behavior
Seriousness of Cheating
Conventional
2.66 (.89)
Digital
2.69 (.90)
t-Statistic
2.29
Cohen’s d
0.03
Unpermitted collaboration
2.24 (.86)
2.26 (.87)
1.56
0.02
Plagiarized a few sentences
2.83 (.92)
2.85 (.92)
1.79
0.02
Plagiarized a complete paper
3.73 (.67)
3.73 (.69)
0.14
0.00
Used unpermitted notes during a test or exam
3.63 (.71)
3.55 (.76)
-5.94*
-0.11
Copied from someone else during a test or exam
3.69 (.70)
3.68 (.69)
-0.68
-0.01
OVERALL
3.12 (.57)
3.11 (.59)
-0.99
-0.02
Variable
Copied homework
Note. Bonferroni adjustment was used to control for inflation in Type 1 error associated with multiple comparisons:
alpha = .05/6 = .008. Cohen’s d = (M2 – M1) /[( 1² + 2²)/ 2].
* p < .008
Digital technology and academic dishonesty 32
Table 4
Percentages of Student’ Self-Reported Engagement in Conventional and/or Digital Forms of Six Cheating Behaviors
Variable
Neither
63.0
Copied homework
Cheating Behavior
Conventional
Digital
Only
Only
25.7
1.2
Both
10.1
Unpermitted collaboration
53.6
23.6
3.6
19.2
Plagiarized a few sentences
61.8
6.0
7.4
24.7
Plagiarized a complete paper
97.9
1.5
0.2
0.3
Used unpermitted notes during an exam
84.4
4.8
7.3
3.4
Copied from someone else during an exam
90.4
7.8
1.0
0.8
OVERALL
32.0
18.2
4.2
45.6
Table 5
Percentages of Student’ Self-Reported Engagement Three Types of Cheating Behavior by Three Cheating Subgroup
Variable
Assignment cheating
Plagiarism
Test cheating
Conventional
Only
(n= 237)
85.2
Cheating Subgroups
Digital
Only
(n= 55)
41.8
Con-Dig
Both
(n = 595)
85.4
2
(2, N= 886)
68.10***
21.2
52.7
71.3
172.91***
8.5
16.4
29.6
81.31***
Note. Cells that are underlined/italicized indicate where chi-square analyses revealed the observed count to be
significantly greater/less than expected count given the marginal frequencies.
*** p  .001
Digital technology and academic dishonesty 33
Table 6
Omnibus MANOVA Results: F-values and for Cheating Group, School and their Interaction
Main and Interaction Effects
Cheating Group
Construct Set
Technology Attributes
Cheating Beliefs
Moral Judgments
Peer Norms
F
1.40
19.48***
15.79***
20.73***

.00
.05
.04
.05
School
F
0.75
0.01
0.31
8.98***
Group X School

.00
.00
.00
.02
F

0.39
1.42
0.27
0.83
.00
.00
.00
.01
Note. F-values are Wilks’ Lambda. The main effect for school on peer norms is a .2 to .3 increase (across
all four cheating subgroups) on perceptions of peer cheating behavior.
*** p  .001
Digital technology and academic dishonesty 34
Table 7
Mean Group Comparisons of Cheating Beliefs, Moral Judgment/Regulation, and Peer Norms by Four Cheating Groups
Cheating Subgroup
Outcome
NonCheater
(n= 418)
Conventional
Only
(n= 237)
Digital
Only
(n= 55)
ConventionalDigital
(n = 595)
Univariate
F(3,1295)

2.82 a
2.45b
2.61b
2.22C
50.32***
.12
(.61)
(.50)
(.62)
(.54)
3.44 a
3.43a
3.22b
3.14b
17.22***
.04
(.61)
(.50)
(.62)
(.54)
3.72 ab
3.75a
3.61ab
3.53b
7.31***
.02
(.63)
(.47)
(.57)
(.54)
4.58ab
4.50b
4.73a
4.27c
19.90***
.04
(.69)
(.60)
(.37)
(.76)
1.53 a
1.77b
1.72ab
1.93b
22.92***
.05
(.68)
(.66)
(.65)
(.75)
2.29a
2.57b
2.72bc
2.85c
31.26***
.07
(.87)
(.75)
(.96)
(.91)
3.14a
3.29ab
3.12a
3.43b
11.71***
.03
(.88)
(.77)
(.77)
(.82)
Cheating Beliefs
Assignment cheating
Plagiarism
Test cheating
Moral Judgment/Regulation
Moral responsibility
Neutralize responsibility
Perceptions of Peer Norms
Acceptability of dig cheating
Cheating behavior
Note. Tukey Honestly Significant Differences were used to test between group differences. Groups with different superscripts
for a particular variable are significantly different from one another at the p  .05 level.
*** p  .001
Digital technology and academic dishonesty 35
Table 8
Correlation Matrix, Means, and Standard Deviations
Variable
1. Conventional cheater
1
–
2
2. Digital cheater
.58
–
3. School (public=1)
.03
-.07
–
4. Sex (male=1)
.05
.04
.01
–
5. Year in college
.08
.04
-.06
.01
–
6. Technological ability
.01
.03
-.07
.36
.03
–
7. Concerned about being monitored
-.03
.01
-.06
.01
-.05
.06
–
8. Seriousness of cheating behavior a
-.25
-.26
-.01
-.10
.07
-.04
.01
–
9. Judgment of moral responsibility
-.18
-.17
-.03
-.14
.04
-.04
.02
.36
–
10. Neutralization of responsibility
.22
.20
.04
.09
-.09
.07
.02
-.41
-.50
–
11. Peer acceptability of digital cheating
.23
.25
.02
.18
-.06
.13
-.05
-.29
-.34
.52
–
12. Peer cheating behavior
.14
.12
.19
-.12
.14
-.06
-.06
-.02
-.10
.04
.06
–
0.64
0.48
0.50
0.50
0.66
0.47
0.41
0.49
2.87
1.14
2.98
0.76
3.16
0.87
3.11
0.58
4.43
0.71
1.76
0.73
2.62
0.90
3.29
0.84
M
SD
3
4
5
6
7
8
9
10
11
12
Note. N = 1,205-1,305. Conventional and Digital Cheater are coded 0 = Did not cheat, 1 = Cheated. All boldface correlations statistically
significant: any correlation r = .06 or greater, p ≤ .05; r = .08 or greater, p ≤ .01; and r = .10 or greater, p ≤ .001.
a
Students’ ratings of the seriousness of conventional and digital cheating behaviors were highly correlated (r= .94). The variable used in these
correlational analyses is the mean of all 12 behaviors assessed in this study.
Digital technology and academic dishonesty 36
Table 9
Multinomial Logistic Regression Predicting Cheating Group Membership (with Non-cheaters as the Reference Group)
Conventional Only
Measure
b
SE
Odds
ratio
Digital Only
b
Conventional-Digital
SE
Odds
ratio
b
Model Fit Statistics
SE
Odds
ratio
-2 Log
Likelihood
2
(3, N=1305)
Demography
School (public=1)
0.56**
0.21
1.74
-0.15**
0.33
0.86
-0.35*
0.16
0.71
2481.84
22.77***
Gender (male=1)
0.26
0.20
1.31
0.08
0.34
1.08
0.09
0.17
1.09
2460.67
1.77
Year in college
0.22**
0.08
1.24
-0.01
0.13
0.99
0.21**
0.07
1.23
2472.00
13.11**
Technological ability
1.05
0.13
0.86
-0.07
0.09
0.93
-0.08
0.11
0.92
2460.34
1.45
Concerned monitored
-0.21*
0.11
0.82
-0.24
0.18
0.79
0.06
0.09
1.06
2468.18
9.28*
Seriousness of cheating
-0.60**
0.21
0.55
-0.71*
0.32
0.49
-1.08***
0.17
0.34
2507.39
48.50***
Moral responsibility
0.26
0.18
1.29
1.19**
0.38
3.30
-0.09
0.14
0.92
2477.85
18.96***
0.41*
0.17
1.50
0.38
0.26
1.46
0.23
0.15
1.26
2465.07
Acceptability of dig cheat
0.17
0.12
1.19
0.57**
0.20
1.77
0.52***
0.10
1.69
2489.73
30.84***
Peer cheating behavior
0.13
0.11
1.13
-0.05
0.20
0.95
0.43***
0.10
1.53
2483.36
24.46***
Technology
Normative Judgments
Moral regulation
Neutralize responsibility
6.18
Perceptions of Peer Norms
Note.
* p ≤ .05
** p ≤ .01
*** p ≤ .001