Academic integrity: Online classes compared to face-to-face classes

Abstract

Trends toward an increase in online courses suggest the need for more research on differing levels of cheating and other acts of academic disintegrity as compared to face-to-face classes. We surveyed 639 students in both types of classes. Students felt it was easier to cheat in online classes than face-to-face classes. For students taking both online and face-to-face classes, we found that cheating occurred more frequently in online classes. However, students who took only online classes were less likely to cheat than students who took only face-to face classes. The relationship of age to taking online classes and cheating offered an explanation for the contradictory finding. Sex differences in cheating behavior were absent.

Full text

Academic Integrity: Online Classes Compared to
Face-to-Face Classes
Arden Miller and Adena D. Young-Jones
Trends toward an increase in online courses suggest the need for more research on
differing levels of cheating and other acts of academic disintegrity as compared to
face-to-face classes. We surveyed 639 students in both types of classes. Students felt
it was easier to cheat in online classes than face-to-face classes. For students taking
both online and face-to-face classes, we found that cheating occurred more frequently
in online classes. However, students who took only online classes were less likely
to cheat than students who took only face-to face classes. The relationship of age
to taking online classes and cheating offered an explanation for the contradictory
nding. Sex differences in cheating behavior were absent.
Arden Miller and Adena D. Young-Jones,
Psychology Department, Missouri State Uni-
versity.
Correspondence regarding this article should
be addressed to Arden Miller at ArdenMiller@
MissouriState.edu.
Paper presented at the meeting of the South-
western Psychological Association, April, 2012,
Oklahoma City, OK.
Since 2003, online enrollments have
grown 358%, and 31% of students now take
at least one course online (Allen & Seaman,
2011). But this research also shows that
about a third of academic leaders perceive
online outcomes to be inferior to traditional
classes and that faculty members have misgiv-
ings about online classes. These misgivings
include lack of course comparability, more
opportunities to cheat in online classes, and
a greater attraction to students whose goal
is to cheat (Bailey & Bailey, 2011). Young-
berg’s (2012) commentary in the Chronicle
of Higher Education argues that the number
one reason why online education will not
replace college is “It’s too easy to cheat.”
The majority of faculty (64%) and students
(57%) believe it is easier to cheat in online
classes (Kennedy, 2000).
Despite this common belief, there is a
lack of adequate research comparing aca-
demic disintegrity online (OL) to face-to-face
(FF) classes. Existing research has found
higher levels of cheating in online classes
(Lanier, 2006). But others have found lower
levels of cheating in online classes (Hart
& Morgan, 2010; Kidwell & Kent, 2008,
Stuber-McEwen, Wisely, & Hoggatt, 2009) or
cheating levels comparable to other research
studies of FF classes (Grijalwa, 2006, Watson
& Sottile, 2010). But comparing ndings
to other studies that estimate cheating in
traditional classes, as Grijalwa did, is a weak
methodology not suited to hypothesis testing.
Research has found lower levels of cheat-
ing in online classes may have been subject
to volunteer biases that inuence ndings. In
Hart’s (Hart & Morgan, 2010) study, the 44
participating students from traditional classes
represented 44% of the cohort, while the 330
students from online classes represented only
16% of the cohort. Similarly, Stuber-McEwen
et al. had a 100% response rate with face-
to-face students completing the survey in
class. Online students were emailed a link
from instructors, but the authors apparently
didn’t know how many were sent the emailed
in order to report a response rate. However,
surveys done through email links with no
incentive typically have a very low response
rate; often less than 10% (Miller, Shoptaugh,
& Parkerson, 2008). Research shows that
volunteerism is related to higher levels of
altruism (Rosenthal & Rosnow, 1975), and
thus may be selective for lower levels of cheat-
ing (Miller, et al., 2008). The online class

2/ Journal of Instructional Psychology, Vol. 39, No. 3
cheating assessments, having lower response
rates, would be more signicantly reduced
by volunteer biases. Similarly, Kidwell and
Kent (2008) had a much higher response rate
among traditional students (42%) relative to
online students (24.8%).
When comparing online students to
traditional students, the glaring differences
in attributes of online versus face-to-face
students that are plausible explanations for
any differences are often missed. Residential
students are more likely to be in the 18-22
range, while non-traditional and older stu-
dents are more likely to be enrolled in online
classes. For example, Dutton, Dutton, and
Perry (2002) found that the average age of
their students in lecture classes was 22.5 as
compared to the average age in online classes
of 27.6. Previous research has demonstrated
that older students are less likely to cheat
(Miller, Shoptaugh, & Parkerson, 2008). Past
research has also indicated that, in general,
undergraduates members of Greek social
organizations tend to cheat more (Iyer &
Eastman, 2006), and these students are likely
to be traditional, face-to-face students. A
variety of other attribute characteristics that
may differ between online and face-to-face
students could be determining factors behind
the inconsistency of ndings regarding cheat-
ing in online and face-to-face classes.
What seems to be missing from these
comparison studies is the fact that many stu-
dents take both sorts of classes. The benet
of surveying these students resides in the
control of attribute differences between online
and traditional classes, making the students
their own control. Our present research will
consider differences in cheating during online
and face-to-face classes for students enrolled
in both types of classes. We will also consider
between subject comparisons for students
having only one type class.
Additionally, we investigated whether
there were differences in online and face-
to-face student’s perceptions of how severe
consequences should be and beliefs about the
student’s responsibility to prevent cheating.
Previous research has found students who
cheat more believe consequences should
be less severe (Kufahl, Shoptaugh, Miller,
& Levesque, 2005) and demonstrate lower
levels of Academic Integrity Responsibility
(Miller, Shoptaugh, & Wooldrige, 2011).
Academic Integrity Responsibility (AIR) is
the extent to which it is believed that students
are responsible for deterrence of cheating
in coursework. Low scores indicate the
belief that promotion of academic integrity
is primarily or solely the responsibility of
the teacher.
The purpose of this study was to compare
online course cheating to face-to-face course
cheating using between subjects (students
enrolled in only one type) and within sub-
jects (students enrolled in both online and
face-to-face classes) comparisons, with an
established survey (Miller, Shoptaugh, &
Wooldridge, 2011). The survey has extra
items added to accommodate differences in
cheating that occur in an online class. Ad-
ditionally, comparisons will be made on the
AIR (Miller, et al., 2011).
Method
Participants
Participants were 531 undergraduates
and 108 graduate students from two south-
midwest universities. Extra credit was given
as determined by their individual instructor.
While 144 were solicited through an introduc-
tory psychology pool at one university, 279
participants from the same university and
214 participants from the second university
volunteered with varied incentives offered by
their instructor. Students were sent to a web
page that provided the consent form, with
consent acknowledged by entering the survey
web form. Median age was 22 with a range
from 17 to 56 with 67.5% of participants
being female. We received 639 responses.
Participants were fairly evenly distributed
across college class. Of these, 289 had both
types of classes, 246 had only face-to-face

E . . / 3
Table 1
Individual Cheating Item (1 = never) Differences Within Students Having Both Types of
Classes and Between Groups for Students with Only One Type of Class
Both
Types One Type
OL*FF OL*FF
1. Turning in work done by someone else. 1.06 1.07 1.01*1.09
2. UNauthorized use of the text or other book in answer-
ing items on a test, quiz, or other assessment. 1.45*1.14 1.42 1.20
3. UNauthorized use of a web search or other digital me-
dia in answering items on a test, quiz, or other assessment. 1.50*1.22 1.38 1.26
4. Writing or providing a paper or assignment for another
student. 1.17 1.12 1.06*1.25
5. Receiving help on an assignment that exceeds that
which would be acceptable to the teacher. 1.39 1.32 1.11*1.53
6. Getting questions or answers from someone who has
already taken a test. 1.34 1.41 1.06*1.50
7. Providing questions or answers to a student who will be
taking the test at a later time. 1.35 1.39 1.10*1.54
8. Helping someone else cheat during a quiz or exam. 1.20*1.12 1.07*1.23
9. Copying or getting help from another student during a
quiz or exam. 1.23 1.16 1.11 1.24
10. Paraphrasing (copying with rewording) a sentence
from a written or internet source without footnoting or
referencing it in the paper.
1.47*1.36 1.36*1.60
11. Copying a sentence directly from a written or internet
source without quotes and proper referencing. 1.20 1.20 1.14*1.31
12. Turning in a paper obtained in large part from a term
paper “mill” or website. 1.06 1.07 1.00*1.06
13. Using unpermitted crib notes (or cheat sheets) during
a test. 1.20*1.11 1.10 1.13
14. Altering a graded test and submitting it (as misgraded)
for extra credit. 1.07 1.05 1.00*1.07
15. Turning in a paper copied, at least in part, from an-
other student’s paper. 1.10 1.08 1.02 1.07
16. Using a false excuse to obtain an extension on a due
date or to take a test at a different time. 1.19 1.14 1.06*1.24
17. Participating in the exchange or sharing of a stolen
copy of the test. 1.08 1.05 1.02 1.09
18. Turning in a paper that you originally wrote for
another class without awareness of the professor regarding
its previous use.
1.14 1.16 1.04*1.20
* = p < .01 two tailed

4/ Journal of Instructional Psychology, Vol. 39, No. 3
classes, and 104 had only online classes.
Procedures
All items were completed in an html
formatted web survey. At the outset it was
made explicit that all responses were entirely
anonymous. The anonymous survey included
18 items to address categories of cheating
with choices of: “never”, “once”, “more than
once”, or “frequently”. With permission,
these items were derived from McCabe’s
surveys that have been widely used (McCabe
& Trevino, 1993). However the items have
evolved through two research studies (Miller,
Shoptaugh, & Parkerson, 2008; Miller,
Shoptaugh, & Wooldridge, 2011) and were
updated to address both online and face-to-
face classes, see Table 1. Participants also
completed a ve–item survey to assess Aca-
demic Integrity Responsibility (AIR) (Miller,
Shoptaugh, & Wooldridge, 2011). Students
were asked how often they witnessed cheat-
ing in the past year using the same choices
as above and whether they thought it was
easier to cheat in online classes (1 = strongly
disagree, 5 = strongly agree). Students also
gave their sex, age, class, and GPA.
After the anonymous survey was com-
pleted, students were taken to a new web
form which allowed them to enter their names
into a second database in order to receive
participation credit.
Results
Frequency of Cheating
While 15.7% disagreed, 57.2% agreed
that is easier to cheat in online classes. We
analyzed the accuracy of that belief in two
ways. Within-subject comparisons were made
with students having both types of classes
followed by between-subjects comparisons
for students having only online (OL) or only
face-to-face (FF) classes.
Table 2
Differences in Online Only, Face-to-face Only, and Student with Both Types of Classes
Class Type
Online
(N = 104)
Face-to-Face
(N = 246)
Both
(N = 289)
Variable M (SD) M (SD) M (SD)
Self-reported Cheating * 2.52 (4.45)a4.66 (6.18)b4.81 (6.44)b
Age ** 28.8(7.90) a 21.2(5.36) b 23.65(5.65) c
AIR (p < .05) 16.6(4.84) a 15.1(4.44) b 15.44(4.40) a b
Online Cheating is easier ** 3.08(1.31) a 3.88(1.13) b 3.72(1.20) b
Witnessed cheating** 1.74(1.06) a 2.29(1.11) b 1.99(1.03) b
* Difference signicant at the p < .01 level
** Difference signicant at the p < .001 level
abc Means with the same letter do not differ on Scheffe test

E . . / 5
Students taking both types of classes
reported more cheating in OL classes, M =
4.15 than in FF classes, M = 3.15, t (288) =
4.35, p < .001. The fact that these subjects
took signicantly more FF credits, M = 21.9,
than OL credits, M = 17.8, t (289) = -6.73, p
< .001, demonstrates that cheating frequency
ndings cannot be explained by differences
in number of credit hours completed. To the
contrary it raises the possibility that these
differences could be underestimated.
Secondly, we made between subject
comparisons for students having only one
type of class. We found lower rates of cheat-
ing in the only-OL students, M = 2.52, than
in only-FF students, M = 4.66, t(265.4) =
-3.64, p < .001. Number of hours were not
signicantly different, t(348) = -1.85.
Since the between subjects ndings
differed from within subjects ndings, we
explored the most obvious attribute difference
between online only and face-to-face only:
age. Our introduction reviewed the evidence
that online students are older on the average
and that older students cheat less. When
age was entered into the regression alone,
the standardized regression coefcient was
substantial, b* = -.235, t(346) = -4.51, p <
.001. When entering class type second in the
regression, the effects the differences between
the two groups was no longer signicant, b*
= .069, t(345) = 1.16.
Literature often describes cheating data
in percentages who have cheated. Fewer OL
only students cheated, 51.9% than FF only
students 71.5%, χ2(1) = 12.49, p < .001.
This is likely due to the older age of the OL
only students. For students with both types of
classes, we compared cheating within subjects
and found more students had cheated OL
classes, 64.7%, than in FF classes, 49.1%,
χ2(1) = 14.3, p < .001.
An item by item view of differences
for each type of cheating behavior in Table
1 shows how these specic behaviors dif-
fer in OL and FF classes. In general there
appears to be more unauthorized use of the
crib notes, text, and web searches in online
courses for students taking both types of
classes. However, students in only face-to-
face classes are more likely to use someone
else’s work or provide it to another student,
receive improper help in completing an as-
signment, get questions from those who have
taken the test and give questions to others,
and misuse the internet relative to students
who take only online classes.
Differences in Online, Face-to-Face, and
Students with Both
To conduct an analysis of variance
comparing the three groups, a cheating score
for students with both types of classes was
counted as their highest cheating rate for either
the OL courses or the FF courses. Signicant
ndings were explored using Scheffe post hoc
tests. Students in OL courses cheated less than
others, F(2, 636) = 5.90, p < .01, see Table
2. Students taking OL classes were older,
F(2, 633) = 59.31, p < .001 and witnessed
less cheating in the past year, F(2, 636) =
10.9, p < .001. They were more inclined to
take responsibility for the integrity environ-
ment, scoring higher on Academic Integrity
Responsibility (AIR), F(2, 635) = 4.11, p <
.05. OL-only students were less likely to
believe that it is easy to cheat in OL than in
FF classes, F(2, 628) = 16.3, p < .001.
Sex Differences
There were no signicant sex differences
or interactions with sex for any measures of
cheating behavior. Females scored higher
on AIR, M = 15.9 than males, M =14.73,
t(634) = -2.95.
To consider arguments that differences in
ndings on sex often follow from differences
in populations, we analyzed sex differences
in cheating for each student source. While
there was a non-signicant trend for males,
M = 5.33, to cheat more than females, M =
3.95, in the population from the second uni-
versity, t(212) =1.58, the opposite marginally
signicant trend, females cheating more, M =

6/ Journal of Instructional Psychology, Vol. 39, No. 3
5.96 than males, M = 3.95, was found among
introductory psychology students at the rst
university, t(142) =1.68, p < .10, with no
such trends in the second population at the
rst university, t(277) = .32.
Other Correlations
Older students were less likely to cheat,
more likely to take responsibility for academic
integrity, perceived consequences should be
more severe, and witnessed less cheating, see
Table 3. This table shows a variety of correla-
tions relevant to understanding cheating in OL
and FF classes. Higher Academic Integrity
Responsibility is related to a preference for
more severe consequences, less cheating, and
less witnessing of cheating.
Discussion
Within the academic community, it is
commonly believed that cheating is more
likely to occur in online classes than face-
to-face classes. Such pervasive notions exist
despite a lack within the literature to support
this comparative idea. Our study builds on
previous research, which has attempted to
compare OL and FF cheating, by using a
between subjects and within subjects design
of participants taking both types of classes (n
= 289), only FF (n = 246), and only OL (n =
104). While the overall consensus agreed that
cheating is easier in online classes (57.2%),
there is a level of complexity to this asser-
tion. Specically, our ndings indicate that
students taking both types of classes are
more likely to cheat in their online classes.
However, a seemingly contradictory nding
occurred when we considered students who
only took OL or only FF classes, because stu-
dents who took only OL classes cheated less
than other students. The ndings showed that
the population who take only online classes
are older, take more Academic Integrity
Responsibility, and cheat less.
The present research supports previ-
ous ndings that cheating occurs within the
academic setting. However, specic cheating
behaviors differ for students taking both types
of classes and only FF courses. Students in
both types of classes were signicantly more
likely to report the usage of cheat sheets
during tests, paraphrasing without proper
Table 3
Correlations Between Selected Variables
AIR 2 3 4 5 6
1. AIR - -.275* .369* -.339* -.116*
-.083
2. Cheating - -.276* .376* .150*
0.058
3. Consequence - -.137* 0
0.018
4. Witnessed - .198*
-0.062
5. OL cheating easier -
-0.077
6. GPA
-
* p < .01

E . . / 7
citation, assisting others cheat, and unauthor-
ized use of text or web in answering items.
An overlap occurs for only FF students in
helping someone else cheat and paraphrasing
without appropriate citations. Additionally,
only FF students are more likely to turn in
work done by someone else, complete work
for someone else, give/receive inappropriate
help, use a false excuse, or submit previous
work in subsequent classes.
The pattern of correlations suggests
that there is a culture or social component
to cheating. Students who cheat more also
witness more cheating and do not perceive
they have any role in reducing cheating. This
could suggest acceptance of cheating in many
academic subcultures. Findings of higher
rates of cheating in fraternities and sororities
supports the notion of disintegrity-accepting
subcultures (Iyer & Eastman, 2006).
While some studies report males cheat
more than females, and a sex differences is
often presumed, many studies, including this
one, failed to nd sex differences in cheating.
Miller, et al. (2008) argue that the differences
in these ndings occur primarily due to sex
differences in volunteerism and as these dif-
ferences are very small and unreliable; sex
should not be considered a signicant factor in
cheating behavior. The fact that three different
sources for participants resulted in minimal
but diverse sex differences underscores the
weakness of any expectations about cheating
behavior as a function of sex.
While we found signicant results in the
present study, limitations exist regarding the
nature of sampling. Participants volunteered
for extra credit points; individuals who desire
extra credit may have different characteristics
than those who do not wish to participate.
While using non-volunteers is ethically
problematic, varieties in incentive strength
may inuence the responding population (cf.,
Miller et al, 2008). Additional research should
also extend the understanding of disintegrity
subcultures and explore methods to prevent
such disintegrity. As there is an increasing
trend toward online courses, extended re-
search within this domain is necessary.
An additional weakness resides in the
selection of disintegity survey items. The
more comprehensive the survey, the higher
the rates of cheating that are typically reported
(Miller et al., 2008). If the survey were more
comprehensive in covering forms of cheating
common in one type of class than in another,
this could generate signicant differences in
cheating rates. Particularly when we consider
differences in how students might cheat in an
online class, attention must be paid to com-
prehensive coverage in surveying disintegrity.
It is common in the literature to report
cheating as percentages of students who have
cheated and we included that statistic in our
results. Although that is useful for compar-
ing the results of different studies, it can be
misleading. If a treatment reduced students
cheating from 12 times per semester to one
or two times per semester, it would not im-
pact the percentage who have cheated. Yet
treatments to prevent frequent cheating are
probably more important than a treatment
that affects a person who would cheat once.
Unfortunately, a common metric, while desir-
able for discussions, is not very practical for
testing hypotheses.
Regardless of teaching modality, educa-
tors should be aware that cheating occurs at
rather high levels. Overall, despite perpetual
reminders that disintegrity is not acceptable, it
is actually quite common within the academic
setting. Deterrence of cheating in online
classes requires attention to new strategies
that may be different from conventional
classes. It appears that professors must be
as, or more, vigilant in addressing cheating
in online classes.
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