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The use frequency of 10 different methods for preventing and detecting academic dishonesty and the factors influencing their use


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This study examines the use frequency by German faculty of 10 different methods for preventing and detecting cheating on exams, plagiarism, and falsification and/or fabrication of data. It also investigates the factors influencing their use. In total, 3655 faculty members from 55 randomly chosen disciplines at 4 German universities were contacted and asked to participate in a web-based survey. Our results show that some methods were applied (very) seldom (e.g. the use of text-matching software), while others were used more frequently (e.g. employing a sufficient number of supervisors for exams). Factors found to promote the increased use of many of these methods include those methods' perceived efficacy as well as external expectations that they be used. When the effort involved in applying a specific method is perceived as high, the frequency of use is reduced. Our results can help universities to improve the prevention and detection of academic dishonesty.
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Studies in Higher Education
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The use frequency of 10 different methods for
preventing and detecting academic dishonesty
and the factors influencing their use
Sebastian Sattler, Constantin Wiegel & Floris van Veen
To cite this article: Sebastian Sattler, Constantin Wiegel & Floris van Veen (2015):
The use frequency of 10 different methods for preventing and detecting academic
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The use frequency of 10 different methods for preventing and
detecting academic dishonesty and the factors inuencing their use
Sebastian Sattler
*, Constantin Wiegel
and Floris van Veen
Institute for Sociology and Social Psychology, University of Cologne, Greinstrasse 2,
50939, Cologne, Germany;
International Institute for Empirical Social Economics,
Haldenweg 23, 86391 Stadtbergen, Germany;
Faculty of Sociology, University of Bielefeld,
P.O. Box 10 01 31, D-33501 Bielefeld, Germany
This study examines the use frequency by German faculty of 10 different methods
for preventing and detecting cheating on exams, plagiarism, and falsication and/or
fabrication of data. It also investigates the factors inuencing their use. In total,
3655 faculty members from 55 randomly chosen disciplines at 4 German
universities were contacted and asked to participate in a web-based survey. Our
results show that some methods were applied (very) seldom (e.g. the use of text-
matching software), while others were used more frequently (e.g. employing a
sufcient number of supervisors for exams). Factors found to promote the
increased use of many of these methods include those methodsperceived
efcacy as well as external expectations that they be used. When the effort
involved in applying a specic method is perceived as high, the frequency of use
is reduced. Our results can help universities to improve the prevention and
detection of academic dishonesty.
Keywords: academic dishonesty; cheating prevention; plagiarism; cheating on
exams; falsication of data; fabrication of data
This study pursues two major aims: rst, mapping the frequency with which faculty of 4
German universities uses 10 different methods of preventing and detecting academic
dishonesty (AD) and, second, investigating ve potential factors that inuence the
(non-)use of these methods. Based on the ndings, examples of practical implications
are discussed.
Existing research shows that some faculty members apply methods to detect and
prevent AD inconsistently, fail to apply sufcient methods, or even do not take
action against AD (Zobel and Hamilton 2002; Pickard 2006; Wilkinson 2009). This
varies among universities, disciplines, etc. (Volpe, Davidson, and Bell 2008; Peled,
Barczyk, and Sarid 2012).
For example, a British survey found that in their attempts to discover plagiarism,
80% of faculty used relatively inefcient means, such as intensive reading (Dordoy
2002). Similar results were found in a German survey (Sattler 2007). The latter
survey also revealed that faculty investigated only one paper in four for plagiarism.
At an Australian university, Wilkinson (2009) found that a third of faculty believed
© 2015 Society for Research into Higher Education
*Corresponding author. Email:
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that their colleagues failed to control for plagiarism entirely. In another study fewer than
10% of faculty used search engines 71% of faculty from six universities in Germany,
Israel, and the USA did not discuss AD for more than 30 minutes in their courses
(Peled, Barczyk, and Sarid 2012). Moreover, a study by Graham et al. (1994,as
quoted in Coalter, Lim, and Wanorie 2007; cf. Coren 2012) showed that only 9% of
faculty penalized cheating students.
It has been assumed that the failure to prevent and/or detect AD can encourage
students to see AD as an accepted practice or to believe there is little or no risk of
detection (Jendrek 1989; Volpe, Davidson, and Bell 2008; Dee and Jacob 2010;
Teh and Paull 2013). Research shows that this assumption holds, since the likelihood
of AD increases if prevention and detection methods are not (sufciently) used (Mara-
mark and Maline 1993; Kerkvliet and Sigmund 1999; McCabe, Treviño, and Butter-
eld 2001).
This limited detection and prevention is surely one reason for the widespread preva-
lence of AD on campuses worldwide. A recent study at four German universities found
that 75% of the students admitted that they had conducted at least one of seven inves-
tigated behaviors within the past six months, including plagiarism, cheating on exams,
and falsifying/fabricating data (Patrzek et al. 2015, 7). Other self-report studies found
similarly high rates (Whitley 1998; Rettinger, Jordan, and Peschiera 2004; Vowell and
Chen 2004;ORourke et al. 2010). Surveys among faculty members, who represent one
important agency for counteracting these rates, show that they also observe or suspect a
widespread prevalence. For example, 70% of faculty in a US study reported problems
with AD at their institutions (Frost, Hamlin, and Barczyk 2007). In another study, 41%
of the surveyed US faculty members had personally observed students copying on
exams over a period of three years, 80% had discovered instances of studentspara-
phrasing or copying sentences from written sources without citation, and 21% knew
of students who had fabricated or falsied lab data (McCabe 2005). Pickard (2006)
has stated that in a study in the UK an overwhelming 72% of faculty reported detecting
at least one case of plagiarism during the previous year.
This widespread and continuous violation of academic integrity undermines major
goals of education in general and of universities in particular (e.g. Keith-Spiegel et al.
1998, Whitley 1998; Sattler, Graeff, and Willen 2013; Patrzek et al. 2015). For
example, it interferes with the internalization of norms of good scientic practice,
general learning progression, and thus the acquisition of human capital (Bouville
2010; Dee and Jacob 2010; Bretag 2013). It thus results in unreliable grading
(Magnus et al. 2002) and also distorts the feedback faculty members receive about
the efcacy of their teaching (Bouville 2010). Moreover, failure in learning increases
the likelihood of failure in later professional life (Teixeira and Rocha 2010). Research
also indicates that such learnedbehavior is likely to be transferred to other spheres of
life, resulting in, for example, workplace deviance (Whitley 1998; Nonis and Swift
2001; Lawson 2004). Another consequence of not detecting and punishing cheaters
could be that a small number of cheating students leads their honest peers to believe
that they are at a disadvantage in the competition for grades and jobs; this can
lead to a contagion effect and partially explain the high prevalence rates reported
(Keith-Spiegel et al. 1998; Dee and Jacob 2010; Sattler, Graeff, and Willen 2013;
Hamlin et al. 2013). In the long run, widespread AD especially if exposed by the
media can also result in reducing the value of academic degrees and public support
of higher education (Bretag 2013; cf. Martinson, Anderson, and De Vries 2005;
Keith-Spiegel et al. 1998).
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As a result not controlling for or even passively allowing AD could be seen as a vio-
lation of ethical standards in teaching and procedures that endorse academic integrity, as
manifested in institutional policies and the guidelines of scholarly associations (Coalter,
Lim, and Wanorie 2007; Bouville 2010; Peled, Barczyk, and Sarid 2012). Thus, faculty
is morally responsible for the prevention and detection of AD and are required to
emphasize ethical behavior by following and enforcing the policies (cf. Jendrek
1989; Parameswaran 2007; Peled, Barczyk, and Sarid 2012; Glendinning 2014).
While institutional honesty policies and university-wide honor codes have been
widely researched, less attention has been given to the role of individual faculty
members(Levy and Rakovski 2006, 735). It is thus crucial to investigate why these pol-
icies are often ignored and/or why so little effort is put toward preventing and detecting
AD (Levy and Rakovski 2006). A better understanding of faculty behavior would help
administrators and university ofcials to improve support for faculty (Coren 2012).
Several years ago, researchers called for increasing this understanding by conduct-
ing quantitative research on the use of detection and prevention methods and by exam-
ining why they are not consistently applied by the faculty as a whole (McCabe 1993;
Whitley 1998; Cizek 1999; Dordoy 2002). While several studies have been conducted
in this eld since these appeals, researchers still claim that too little is known about the
role of faculty members actions against integrity violations (Asefa and Coalter 2007;
Volpe, Davidson, and Bell 2008; Coren 2012; Teh and Paull 2013).
In particular, very little research has taken a behavioral perspective to investigate the
decision-making processes involved in faculty behavior. Although a few case studies
exist (Zobel and Hamilton 2002; Zobel 2004), the number of quantitative studies is
limited, especially those that apply a theoretical framework (Keith-Spiegel et al.
1998; Coren 2012). By using a decision-making framework, this study aims to
enhance our understanding of potential drivers and hurdles of the varying use frequency
of 10 different methods for preventing and detecting AD.
Approach and hypotheses
According to theories of rational decision-making (Voss and Abraham 2000;
Kroneberg and Kalter 2012), decisions are a function of an actors preferences and con-
straints, including but not limited to personal, social, and economic factors. These pre-
ferences and constraints are variably distributed among individuals. Therefore, faculty
is likely to perceive different benets and costs involved in using methods of prevention
and detection. Our study investigates ve personal and social factors that could poten-
tially inuence these perceptions, which may consequently increase (due to higher
benets) or decrease (due to higher costs) the use of prevention and detection
methods, namely perceived effort, perceived efcacy, feelings of personal offense,
external expectations, and moral perceptions. These will be described in the following.
Perceived effort
The effort associated with using such methods might be a crucial factor in the decision
to use such methods or not. For example, how time-consuming they are perceived to be
might be extremely important (Larkham and Manns 2002; Zobel and Hamilton 2002;
Teh and Paull 2013). Highly time-consuming methods can be seen as very costly (e.g. if
policies prescribe complex procedures such as documenting evidence for cases of
cheating, attending meetings to discuss the case, etc. (Levy and Rakovski 2006).
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They can also be costly in terms of opportunity costs for academic careers: since pre-
venting and detecting AD might not be advantageous in terms of career advancement
and reputation, some faculty members might prefer to invest their time in fundraising or
writing papers, for example. These assumptions are backed by prior research; Coren
(2011), for example, reports that faculty frequently cites insufcient time as one of
the major reasons they do not take action against AD. Savage (2004) found that
faculty perceives the use of text-matching software to uncover plagiarism as time-
consuming, especially for large courses. Another study showed that one reason why
faculty often ignores AD is the effort involved in dealing with it (Keith-Spiegel et al.
1998). The problem of judging whether something is plagiarism, for example, is a
complex task, adding to the effort and consequently potentially decreasing facultys
commitment to counteracting AD (Keith-Spiegel et al. 1998). We propose the follow-
ing hypothesis (H):
H1: The greater the perceived effort of applying prevention or detection methods, the
lower the frequency of their use.
Perceived efcacy
During decision-making, individuals are also likely to assess the effectiveness of a
certain behavior in terms of achieving a desired outcome, and so faculty may judge pre-
vention and detection methods by their efcacy, i.e. their benets (Coren 2012). The
use of efcient methods may be strongly preferred because facultys jobs are demand-
ing and the time available for preventing and detecting AD is limited. Efcacy may be
particularly important in situations where faculty has a heavy teaching load and are
responsible for a large number of students. In line with this reasoning, Coren (2012)
found that the perceived usefulness of a specic method increased the intention to
use it. Thus, we propose the following hypothesis:
H2: The more efcient prevention and detection methods are perceived to be, the higher
their use frequency.
Feelings of personal offense
Faculty may take personal offense at AD (e.g. if faculty perceives cheating as a viola-
tion of their trust, cf. Levy and Rakovski 2006), which for them thus entails psychologi-
cal costs. Such faculty has an incentive to act in ways that help to avoid this cost, such
as applying methods to prevent and detect AD. This effect has not been tested before,
but given our reasoning, we propose the following hypothesis:
H3: The stronger the feeling of personal offense aroused by AD, the higher the use
frequency of detection and prevention methods.
External expectations
It could be that colleagues, deans, students, or institutional norms (e.g. honor codes or
policies) explicitly expect that faculty uses prevention and/or detection methods, for
example, to uphold and defend university ethics, for the sake of fairness in grading
or of educational equality in general. In a study by Asefa and Coalter (2007),
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respondents strongly agreed that upholding academic integrity was an important part of
their job. On the other hand, Coren (2012) shows that respondents perceived only slight
pressure to deal with AD. Maybe not all deans/department chairs take this issue
seriously (Coalter, Lim, and Wanorie 2007). Thus, the expectation that faculty
members act against AD may vary across and within situations, institutions, and also
cultural contexts. Some ndings indicate that some faculty members primarily try to
handle the issue of AD on their own but others totally ignore it (Wright and Kelly
1974; Jendrek 1989), and therefore clearly violate expectations. Neglecting formal or
informal norms may result in external or internal censure such as social disapproval
on the part of colleagues or psychological costs (cf. Cochran et al. 1999; Posner and
Rasmusen 1999;Opp2013). Conversely, conforming to such norms can result in
psychological rewards such as satisfaction. Research shows that greater social pressure
to counteract AD, and thus perceived behavioral expectations, increases the intention to
act against AD (Coren 2012). Therefore, we propose the following hypothesis:
H4: The higher the external expectations, the greater the use of detection and prevention
Moral perceptions
AD severely violates university rules and ethics. Such rules and ethics are likely to be
part of the value system of academic instructors through processes of internalization.
One indication of this is that almost 40% of the surveyed faculty reported anger and
disgust when they observed AD (Jendrek 1989) and that 71% of faculty expressed
extreme or moderate concern over such AD (Wright and Kelly 1974). Wilkinson
(2009) has also found that faculty considers many forms of AD to be very serious.
Faculty who perceives such AD as morally objectionable is assumed to have an intrin-
sic motivation (cf. Sripada and Stich 2006) to care deeply about adherence to the under-
lying norm (e.g. by detecting AD). Therefore, we propose the following hypothesis:
H5: The more strongly AD is seen as morally problematic, the higher the frequency of
detection and prevention methods.
Design and participants
The sampling procedure we applied had three stages: rst, four German universities
were randomly selected; second, 55 academic disciplines were randomly selected;
and nally, all 3655 faculty members with teaching obligations in the current semester
within these disciplines were contacted. They received a postal pre-notication that
explained the goal of and participation in the web survey. Everyone received a personal
link via email and up to two reminders. Anonymity and voluntariness of participation
were mentioned in every contact and in the declaration of data security. We used the
secure socket layer protocol to guarantee privacy while answering. Universities
never had access to facultys responses. Data collection was controlled by an ofcial
data protection ofcer. At the end of the survey, participants could choose between
four different incentives (5in each case): vouchers for an online store, donations to
a well-known humanitarian association (German World Hunger Organization),
donations to the support association of their university, or money via PayPal. In
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total, 1402 faculty members (38.4%) responded to our survey. The sample size for our
analysis varies due to a list-wise elimination of missing data.
Dependent variables
In our study, we used frequency measures instead of frequently used simple dichoto-
mous questions about the use or non-use of these methods. Therefore, faculty
members self-reported the frequency of their use of a non-exhaustive selection of 10
detection and prevention methods discussed in the literature (Davis and Ludvigson
1995; Whitley 1998; Cizek 1999; Dordoy 2002; Sattler 2007) and considered to be
relevant for our study: four methods against plagiarism, four methods against cheating
on exams, and two methods against the falsication and/or fabrication of data
(see Table 1). This frequency was assessed on 7-point scales ranging from never
(0) to always(6). Only those who reported that they had used the methods of exam-
ination investigated at least once within the current semester were asked about fre-
quency. This ensured that participants were not asked to answer extraneous
questions and that answers would be more reliable and valid. Additionally, respondents
were able to indicate if the use of a specic method was not possible for them. This
could be the case, for example, if their university did not provide text-matching soft-
ware to detect plagiarism. These respondents were dropped from the analysis
because none of the predictors could affect the frequency of use.
Independent variables
Our study assessed two method-specic predictors. Respondents were asked to esti-
mate their perceived effort (i.e. how much time they invested in applying specic
methods) on a 7-point scale ranging from not costly(1) to very costly(7, see
Tables 2 and 3for all independent variables). And they were also asked to evaluate
the perceived efcacy of each method, ranging from not effective(1) to very effec-
tive(7). Additionally, three predictors specic to AD were assessed: For the variable
take offense, faculty was asked to describe how they felt when their students plagiar-
ized, cheated on exams, or falsied and/or fabricated data. This was measured using
Table 1. Descriptive statistics of detection and prevention methods.
Behavior Mean SD N
Reading of term-papers concerning plagiarism mindfully 4.45 1.695 480
Use of search engines to detect plagiarism 2.44 1.891 422
Use of plagiarism detection software 1.38 1.992 323
Demand a statement under oath 3.23 2.710 437
Cheating on exams
Use of different test versions 2.45 2.463 469
Take care that enough supervisors are present 4.94 1.470 449
Take care that students have seats with sufcient distance to each other 5.16 1.264 461
Ensure that forbidden means are not used 5.04 1.258 474
Fabrication/falsication of data
Ensure that no one falsies or fabricates 4.49 1.413 173
Recalculate/control results, data or facts 4.21 1.503 172
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a 7-point scale with the anchors do not feel personally offended(1) and feel strongly
personally offended(7). Respondents were then asked whether checking student
papers for plagiarism, prohibiting cheating on exams, and/or prohibiting falsifying
and/or fabricating data were expected externally. A 7-point scale ranging from not
expected of a faculty member(1) to always expected of a faculty member(7) was
used. Finally, the moral perception of plagiarism, cheating on exams, or falsifying
and/or fabricating data was assessed on a 7-point scale from morally not objectionable
(1) to morally very objectionable(7).
Control variables
All models were controlled for sex, age, and the status of respondents. Sex was coded 0
for malesand 1 for females. Age was assessed using 12 categories from below 20
(0) to 71 and older(11) to ensure anonymity. Status group differentiated between
high(1) for full and assistant professors and low(0) for all others. The portion of
females was 36.6%, the median age 3640 years, and the portion of the low status
staff was 76.5% (see Table 4). This is comparable to the overall population of
faculty members in Germany (portion of females: 33%; mean age: 41 years; portion
of low status staff: 86.7%, own calculation; see Jacob and Teichler 2011).
Table 2. Descriptive statistics of method-specic predictors.
Perceived effort Perceived efcacy
Mean SD NMean SD N
Reading of term-papers concerning plagiarism
3.76 1.855 548 3.69 1.664 544
Use of search engines to detect plagiarism 3.29 1.840 535 4.30 1.383 530
Use of plagiarism detection software 2.99 1.965 522 4.25 1.511 516
Demand a statement under oath 0.84 1.568 537 2.43 1.905 535
Cheating on exams
Use of different test versions 4.25 1.707 538 4.19 1.663 540
Take care that enough supervisors are present 2.54 1.826 534 4.53 1.310 538
Take care that students have seats with sufcient
distance to each other
1.98 1.752 542 4.71 1.205 541
Ensure that forbidden means are not used 2.43 1.686 542 4.58 1.299 540
Fabrication/falsication of data
Ensure that no one falsies or fabricates 3.98 1.584 201 4.13 1.574 195
Recalculate/control results, data or facts 4.50 1.541 195 4.74 1.456 196
Table 3. Descriptive statistics of cheating behavior-specic predictors.
Feeling offended
expectation Moral perceptions
Mean SD NMean SD NMean SD N
Plagiarism 2.68 2.152 573 4.09 1.488 572 5.23 1.129 574
Cheating on exams 1.93 1.910 553 4.71 1.303 550 4.45 1.357 548
of data
3.14 2.309 203 5.07 1.291 202 5.41 0.9410 200
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Statistical analysis
To test our hypotheses, we used multivariate ordinary least-squares (OLS)-regression
models for analyzing predictors of all detection and prevention strategies. Similar pat-
terns were found when using ordered logit regression models (available upon request);
we therefore present OLS-models, which are easier to describe.
Descriptive results
Table 1 shows the average use frequencies for the 10 prevention and detection
methods investigated. Text-matching software is the method used least frequently
to detect plagiarism.
Much more common is the attempt to detect plagiarism by
reading term-papers specically for plagiarism. The method used most frequently
by faculty to detect cheating on exams is insuring that enough proctors are
present. Different test versions are used relatively seldom. More frequently, in
order to detect falsication or fabrication, faculty recalculates or checks students
results, data, and facts. Table 2 shows that the perceived effort and the perceived
efcacy vary strongly among the methods. Demanding a statement under oath is per-
ceived as requiring the least effort and efcacy, while the recalculation/control of
results, data or facts is perceived as entailing the most effort, but is also deemed
most efcient. The respondents felt most offended if their students fabricated/
falsied data, followed by plagiarism and cheating on exams. This corresponds to
the intensity of their moral objections to these types of AD (see Table 3). External
expectations to counteract AD are highest for the fabrication/falsication of data,
followed by cheating on exams and plagiarism.
Table 4. Descriptive statistics for the demographic
variables (N= 1.238).
Variable N%
Below 20
71 and older
Status group
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Multivariate analyses
As predicted in H1, the higher the perceived effort involved in using a method for
detecting or preventing plagiarism, the less frequently that method is used (see
Table 5). Only reading term-papers specically for plagiarism is practiced regardless
of presumed effort (p= .692). Text-matching software is used irrespective of its per-
ceived efcacy to detect plagiarism (p= .121), falsifying H2. But in keeping with H2,
a higher perceived efcacy signicantly increases the use of all other methods. With
reference to feeling offended when students plagiarize, only search engines are used
more often (p= .036, supporting H4). For all methods we found that use frequency
increased when third-parties expected that a method be used. This supports H4. Ver-
ifying H5, our results show that the more strongly plagiarism violates a faculty
members personal morality the more frequently term-papers are read with plagiarism
in mind (p< .001) and written statements under oath are demanded (p= .013). This
was not the case for the use of search engines (p= .477) or software (p= .642). No
sex differences were found. Search engines are less commonly used among older
faculty members than younger ones (p= .010). No further age effects were found.
Only software is used more often among high status faculty members than low
status ones (p= .036).
Cheating on exams
Table 6 shows that all four methods for detecting or preventing cheating on exams
occur more frequently when the perceived effort involved in using them is lower
(p< .001, conrming H1) and their perceived efcacy is higher (p< .001, conrming
H2). The more strongly offended faculty feels about cheating, the more often they use
different test versions (p= .013), but other methods are not affected. It is the reverse
for external expectations. The use of different test versions was not affected (p
= .777), while in keeping with H3 all other methods were used more frequently
when expectations were higher. Moral views about cheating on exams had no
impact on the use of prevention or detection methods (falsifying H4). Female
faculty more often insured that there was sufcient distance between studentsseats
(p< .014) and that prohibited strategies were not used (p< .021). Higher age is associ-
ated with the more frequent use of different test versions (p< .007) but it has a dimin-
ishing effect on ensuring sufcient distance between seats (p< .014). Status had no
effect here.
Falsication or fabrication of data
In contradiction to H1, the perceived effort involved in using methods for detecting
falsication and/or fabrication of data had no effect upon their use (see Table 7).
But a higher perceived efcacy signicantly increased their frequency of use
(p< .001 each). No effects were found for either the degree to which faculty feel
offended by falsication or fabrication of data or for their moral perception of AD
contradicting H3 and H5. Also demographic characteristics are not associated
with use frequency for the methods analyzed. But the higher faculty assesses external
expectations that they use such methods, the more often they are used (conrming
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Table 5. OLS-regression models to assess inuences of the use frequency of different plagiarism detection (D) and preventions (P) methods.
Model 1 Model 2 Model 3 Model 4
Reading term-papers
mindfully (D) Search engines (D) Text-matching software (D) Statement on oath (P)
Beta [95% CI] Beta [95% CI] Beta [95% CI] Beta [95% CI]
Perceived effort 0.016 [0.059 | 0.088] 0.108* [0.202 |0.017] 0.201*** [0.302 |0.093] 0.245*** [0.624 |0.292]
Perceived efcacy 0.367*** [0.295 | 0.470] 0.219*** [0.176 | 0.429] 0.084 [0.029 | 0.249] 0.199*** [0.154 | 0.408]
Feeling offended 0.027 [0.087 | 0.044] 0.100* [0.006 | 0.170] 0.102 [0.006 | 0.195] 0.025 [0.085 | 0.148]
External expectation 0.186*** [1.407 | 0.318] 0.261*** [0.225 | 0.472] 0.256*** [0.201 | 0.495] 0.116* [0.046 | 0.388]
Moral perception 0.105* [0.029 | 0.294] 0.035 [0.222 | 0.104] 0.027 [0.250 | 0.154] 0.123* [0.066 | 0.534]
Female 0.071 [0.038 | 0.530] 0.076 [0.652 | 0.062] 0.072 [0.737 | 0.143] 0.047 [0.767 | 0.253]
Age 0.002 [0.064 | 0.067] 0.128* [0.190 | 0.026] 0.062 [0.156 | 0.046] 0.038 [0.167 | 0.075]
High status 0.006 [0.316 | 0.361] 0.057 [0.179 | 0.678] 0.121* [0.034 | 1.039] 0.035 [0.850 | 0.401]
F(Sig) 18.97*** 10.15*** 6.66*** 8.87***
Adj. R² 0.23 0.15 0.12 0.13
N480 422 323 437
Note: Beta, standardized regression coefcient; CI, condence interval.
*p< .05.
**p< .01.
***p< .001.
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Table 6. OLS-regression models to assess inuences of the use frequency of different detection (D) and preventions (P) methods concerning cheating in
Model 1 Model 2 Model 3 Model 4
Different versions (P) Sufcient distance (P) Enough supervisors (D)
Ensure non-use of forbidden
means (D)
Beta [95% CI] Beta [95% CI] Beta [95% CI] Beta [95% CI]
Perceived effort 0.299*** [0.556 | 0.318] 0.280*** [0.265 | 0.145] 0.143*** [0.186 | 0.046] 0.150*** [0.175 | 0.051]
Perceived efcacy 0.316*** [0.353 | 0.605] 0.274*** [0.202 | 0.381] 0.392*** [0.357 | 0.554] 0.299*** [0.213 | 0.382]
Feeling offended 0.108* [0.030 | 0.250] 0.007 [0.052 | 0.062] 0.054 [0.108 | 0.026] 0.023 [0.041 | 0.071]
External expectation 0.012 [0.186 | 0.139] 0.123** [0.038 | 0.210] 0.105* [0.019 | 0.222] 0.148*** [0.061 | 0.236]
Moral perception 0.052 [0.254 | 0.066] 0.008 [0.092 | 0.076] 0.017 [0.118 | 0.080] 0.032 [0.054 | 0.113]
Female 0.047 [0.209 | 0.713] 0.091* [0.015 | 0.489] 0.064 [0.074 | 0.485] 0.101* [0.041 | 0.509]
Age 0.124** [0.037 | 0.238] 0.114* [0.116 | 0.013] 0.043 [0.090 | 0.033] 0.058 [0.085 | 0.019]
High status 0.054 [0.197 | 0.764] 0.049 [0.113 | 0.375] 0.054 [0.123 | 0.455] 0.015 [0.206 | 0.286]
F(Sig) 15.77*** 16.78*** 15.55*** 14.43***
Adj. R² 0.20 0.22 0.20 0.19
N469 461 449 474
Note: Beta, standardized regression coefcient; CI, condence interval.
*p< .05.
**p< .01.
***p< .001.
Studies in Higher Education 11
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Summary of the results and examples of policy implications
Several studies indicate that AD is a widespread phenomenon at colleges and univer-
sities (Whitley 1998; McCabe 2005). At the same time, several methods for preventing
and detecting AD exist, but are not consistently applied by all faculty members (Volpe,
Davidson, and Bell 2008; Peled, Barczyk, and Sarid 2012). The present research aimed
at mapping the use frequency of 10 methods for preventing and detecting AD and shed-
ding light on factors inuencing their use. With the help of this information policies that
would support a higher penetration of certain methods are discussed. While some of the
policies have already been translated into action at many institutions, at many others
they have not.
We found that the frequency of use of different methods varies greatly. For
example, the method for detecting plagiarism used most frequently by faculty was
reading term-papers carefully; while the use of text-matching software was used less
often. In terms of cheating on exams, faculty most often attempted to insure sufcient
distance between students in exams, and less frequently used different test versions.
We tested ve hypotheses about potential causes of variation in the use frequency of
prevention and detection methods of which no hypothesis was fully conrmed by the
data. Nevertheless, several factors were signicantly associated with many of the
methods investigated.
Perceived effort
The highest perceived effort was found for recalculating/controlling results, data, or
facts, and using different test versions, while the lowest was found for demanding a
Table 7. OLS-regression models to assess inuences of the use frequency of different detection
(D) methods concerning falsication or fabrication of data.
Model 1 Model 2
Ensure that no one falsies or
fabricates (D) Recalculate/control results (D)
Beta [95% CI] Beta [95% CI]
Perceived effort 0.043 [0.072 | 0.149] 0.034 [0.178 | 0.112]
Perceived efcacy 0.511*** [0.362 | 0.596] 0.321*** [0.191 | 0.517]
Feeling offended 0.060 [0.043 | 0.117] 0.061 [0.141 | 0.061]
External expectation 0.214** [0.087 | 0.395] 0.245** [0.108 | 0.477]
Moral perceptions 0.075 [0.093 | 0.330] 0.013 [0.285 | 0.243]
Female 0.039 [0.505 | 0.270] 0.037 [0.613 | 0.373]
Age 0.011 [0.086 | 0.101] 0.115 [0.031 | 0.199]
High status 0.014 [0.465 | 0.377] 0.128 [0.983 | 0.094]
F(Sig) 13.39*** 4.34***
Adj. R² 0.37 0.14
N173 172
Note: Beta, standardized regression coefcient; CI, condence interval.
*p< .05.
**p< .01.
***p< .001.
12 S. Sattler et al.
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statement under oath. In testing the effect of perceived effort on applying a method, the
analyses showed that increasing effort was negatively associated with 7 of the 10
methods investigated (conrming H1). The more time-consuming or strenuous the
methods, the less often they were applied (cf. Keith-Spiegel et al. 1998; Coren
2012). Increased effort can result, for example, from complex procedures and paper-
work prescribed by policies, from difculties judging whether something is dishonest
(e.g. in cases of plagiarism), but also simply from applying a method (e.g. installing/
using software, developing/correcting different test versions). The fact that university
faculty members have highly demanding jobs and often work under strong time con-
straints makes it less likely for them to invest time in activities that do not help
promote their careers (Brown et al. 1986). Using these methods also entails other
opportunity costs, since less time is available for potentially more benecial activities
such as publishing or fundraising. In translating these ndings into possible policy rec-
ommendations, the effort needed to prevent and detect AD should be reduced as much
as possible (cf. Levy and Rakovski 2006). One solution could be for the institutions to
provide the resources needed to apply relevant methods: for example, a sufcient
number of proctors for exams or text-matching software for detecting plagiarism
(Zobel and Hamilton 2002). While some policies have been shown to be successful
in counteracting AD, their strength varies with their implementation (Asefa and
Coalter 2007). Thus, the needed resources should be provided on a sufcient scale
and without bureaucratic hurdles. Moreover, institutions would need to distinguish
between tasks requiring administrative skills and those requiring academic judgment
(Ellis 2012). Institutions could, for example, help faculty members with randomizing
tasks for developing/correcting different test versions or consider implementing
specialized units for detecting plagiarism so that faculty could devote more time to sup-
porting students (e.g. by supervising them) and less to detecting plagiarism. To date, not
all universities require that student papers be submitted in digital form. Doing so,
however, would reduce the effort involved by faculty to scan paper copies or to
request digital versions only when plagiarism is suspected (cf. Crisp 2004; Pickard
2006). Detection of AD could also be facilitated by reducing class size, since faculty
would have more time for more accurate performance assessments of individual stu-
dents. They would thus also be much better acquainted with the performance level
of each student so that marked changes in performance can be detected easier
(Sattler, Graeff, and Willen 2013). Other research has shown that faculty often does
not feel condent detecting and dealing with AD (Zobel and Hamilton 2002; Pickard
2006; Wilkinson 2009); in such cases it is often very time-consuming for faculty to
improve their detection and prevention skills on their own. Institutions could thus
help to assist faculty either by offering online resources (Pickard 2006), providing
workshops to improve their condence, for example, by explaining strategies for unco-
vering plagiarism while reading written assignments (cf. Park 2004), teaching how to
efciently use text-matching software/search engines (Zobel and Hamilton 2002;
Bretag 2013), or providing support by an advisory committee for faculty (Coren 2012).
Perceived efcacy
Demanding a declaration under oath was perceived as least efcient and recalculating/
controlling results, data or facts as most efcient. With the exception of using text-
matching software to detect plagiarism, all methods were applied more often when
faculty rated them as more efcient (conrming H2). The presumption of efciency
Studies in Higher Education 13
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reduces the subjectively perceived expectation that methods might be applied without
attaining the desired goal of prohibiting or revealing AD (Coren 2012). Consequently,
efcient methods are applied more frequently since they promise higher benets from
the same effort, which can be seen as important for faculty who has to prevent and
detect AD frequently among a huge number of students. Reading term-papers for pla-
giarism was done more frequently than using text-matching software, although the
former method might not be very effective in detecting plagiarism (conrmed by our
descriptive ndings about perceived efcacy) especially from unfamiliar sources. It
is also worth pointing out that merely announcing the use of technical aids has a deter-
rent effect upon plagiarism since it raises studentsexpected probability of detection
(Davis and Ludvigson 1995; Sattler 2007; Stapleton 2012). In one study, for
example, students describe the use of scramble tests and several proctors as effective
countermeasures (Hollinger and Lanza-Kaduce 2009). Interestingly, methods that stu-
dents assume to be most effective in that study are those applied least by faculty and
vice versa. Such insights about the real and perceived efcacy as well as potential deter-
rent effects of various methods could be used to better inform faculty (cf. Bretag 2013).
Feeling offended
Faculty felt most offended if their students fabricated/falsied data and least if they pla-
giarized. Stronger feelings of offense generally do not increase the frequency with
which methods of detection and prevention are used. Only in the use of search
engines to uncover plagiarism and different exam versions did we nd the effects
hypothesized in H3. The more strongly offended faculty felt by cheating on exams
and/or by plagiarism, the more often these two methods were used. It can be argued
that the use of these methods reduces psychological costs, since AD is experienced
as offensive and a violation of trust (cf. Levy and Rakovski 2006). However, these
costs seem to play a minor role in deciding whether or not to apply the other
methods. Given the inconsistent ndings, we defer to future research rather than
drawing conclusions.
External expectation
Checking for fabrication/falsication of data was expected most from third-parties and
checking for plagiarism was expected least. We found that in the use of 9 out of 10
methods, greater external expectations were associated with a higher use frequency,
thus H4 was largely conrmed, which is in line with prior ndings (Coren 2012).
One exception was reading term-papers specically for plagiarism. Violating the
expectations of others, for examples, colleagues or superiors and their norms, could
result in internal costs such as feelings of guilt, social sanctions such as disapproval,
or even formal punitive methods (cf. Coren 2012). Given our ndings and the fact
that such expectations seem to vary across and within deviant behaviors, cultural con-
texts, institutions, and situations (Wright and Kelly 1974; Jendrek 1989; Asefa and
Coalter 2007; Coren 2012), such expectations should be institutionalized and enforced
consistently (cf. Zobel and Hamilton 2002), for example, in work contracts, policies,
and in the form of honor codes, which are not common, for example, in Germany.
Research has shown that, for instance, faculty also applies varying, individual de-
nitions of AD as well as individual reactions and sanctions to it (Jendrek 1989;
Zobel and Hamilton 2002; Pickard 2006; Frost, Hamlin, and Barczyk 2007; Wilkinson
14 S. Sattler et al.
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2009). A lack of or unclear expectations can lead to faculty ignoring AD altogether
(Maramark and Maline 1993). Some of the distinct advantages of clear institutionalized
expectations might include a more explicit denition of right and wrong behavior,
clearer procedures when a student is suspected of AD, a dened minimum level of pre-
venting and detecting AD (e.g. a percentage of papers to be controlled with text-match-
ing software), and a clearly dened set of sanctions for different kinds and severities of
AD, but also consequences and procedures if faculty ignores AD (Pavela 1997). Some
existing institutionalized rules, however, need to be overhauled, since not all faculty
members fully understand them or are willing to implement them (cf. Jendrek 1989).
Moral perceptions
Faculty disapproved most strongly of fabricating/falsifying data and least strongly of
cheating on exams. Moral disapproval of AD only encouraged faculty to read term-
papers more often for plagiarism and to require a written statement under oath. The
more strongly plagiarism was seen as morally objectionable, the higher the intrinsic
motivation to apply these two methods (as assumed in H5). Future research needs to
investigate why this was not the case with the other methods.
Limitations and future research
This study has several limitations: rst, opportunity costs of faculty for participating in
our survey are high, consequently not all responded to our survey. However, our
response rate is comparable to previous faculty studies (Nuss 1984; Daniels and
Guppy 1992; Blix et al. 1994) or even higher (Pickard 2006; Hard, Conway, and
Moran 2006; Frost, Hamlin, and Barczyk 2007; Coren 2012). One indication that no
severe sample composition bias occurred is the fact that several demographic character-
istics of our study are consistent with the overall population of faculty in Germany (Jacob
and Teichler 2011). We nonetheless tested whether our results showed composition
biases due to the selective participation of the faculty members. To this end we used
sampling weights (Winship and Radbill 1994) for gender proportion and discipline
afliation, since this information was available for the initial sample. Our recalculation
did not lead to any signicant changes in the results (available upon request). Moreover,
the research is based on data from faculty at several universities and disciplines, which
gives it an advantage over many single-site or case studies.
Second, some faculty members might hesitate to reveal the frequency with which
they used different methods and answers might be affected by social desirability and
thus upward-biased. To minimize such bias, we assured high data security and anonym-
ity (e.g. supervision by a data protection ofcer, using secure sockets layer protocols).
Furthermore, it has been found that the use of web surveys improves the reporting of
sensitive information and leads to higher data quality in comparison to other survey
methods (Kreuter, Presser, and Tourangeau 2008; Crutzen and Göritz 2010).
Third, as our study is cross-sectional, our results are a snapshotof one academic
semester. Longitudinal studies can complement our ndings by assessing changes in
detection and detection behavior over time. This would possess the added benetof
measuring the effectiveness of changes in institutional guidelines for applying such
methods. However, qualitative research or a mixed methods approach could also
help to increase an in-depth understanding of how faculty rationalize their (non-)use
Studies in Higher Education 15
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of prevention and detection methods and to explore facultys ideas about strategies to
counteract AD.
Fourth, due to time restrictions in the survey, we only considered a potentially selective
set of common methods. But many more methods for preventing and detecting AD exist,
such as assigned seating in exams, the use of devices to detect mobile phone use in class-
rooms, and courses in the correct use of citation and academic integrity (Houston 1986;
Davis and Ludvigson 1995;Whitley1998; Cizek 1999; Dordoy 2002; Faucher and
Caves 2009). Future research should also investigate these other methods and the variables
affecting their use as well as verifying the results in other countries and cultural contexts,
because differences might exist in facultys moral perceptions of AD; their perceptions of
personal responsibility for detecting and preventing AD; the legal means for sanctioning
AD, etc. (Dordoy 2002). In this vein, further cultural, legal, institutional, and personal
reasons behind the use or non-use of prevention and detection methods should be
This study showed that the use of different prevention and detection methods varies
strongly depending on the targeted type of AD, the method itself, and also on the
ve investigated theoretically relevant factors. Despite relatively strong moral objec-
tions to AD, not all faculty vigorously engaged in detection and prevention methods.
Their decisions to apply a method were most consistently inuenced by the perceived
effort needed to apply it, its efcacy, and external expectations that it be applied. Given
these results, it could be recommended that several of the examined methods for pre-
venting and detecting AD should be applied more often and consistently. This
should be supported by institutional changes reducing the effort of such methods, by
information about their efcacy, and by stronger expectations to apply them in order
to avoid the negative consequences of AD for all those involved.
We thank all those who helped to conduct this study, especially Dominik Koch, Ines Meyer,
Andrea Schulze, and Sebastian Willen. We gratefully acknowledge the helpful comments of
Donald L. McCabe and the anonymous reviewers. Thanks to Cynthia Hall for editorial
Disclosure statement
The authors declare that there are no conicts.
This work was supported by the Federal Ministry of Education and Research [grant number
01PH08024]. Sebastian Sattlers research was supported by a Rectorate Fellowship from Biele-
feld University [grant number 3521.01] and a postdoctoral Fellowship from the Fritz Thyssen
Foundation and the Cologne Graduate School of Management, Economics, and Social Sciences.
1. Comparisons of the results between prevention and detection methods are limited and
should be interpreted with caution since the number of cases varies between the analysis
16 S. Sattler et al.
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... Se ha detectado un aumento muy considerable de trabajos plagiados tanto en estudiantes como en investigadores, el estudio de sus causas/factores parece muy interesante para esclarecer por qué se han disparado los trabajos plagiados (Sattler et al., 2017). ...
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El mundo en el que vivimos está inmerso en un gran desarrollo tecnológico donde los alumnos tienen acceso a una gran cantidad de contenidos. Internet es el modo más rápido de acceder a estos contenidos, pero debe de mantener una determinada ‘integridad académica’. A la vez que internet se convierte en una magnífica herramienta de búsqueda de información, también lo hace como una fuente de plagio a la que los estudiantes acuden de forma mayoritaria (Adam, 2016). Los estudiantes a pesar de conocer que es el plagio y sus consecuencias porque, o bien se lo ha explicado un profesor, o bien la Universidad se lo ha dado a conocer en las Normas de buen comportamiento en su entrada a la Universidad, siguen usándolo. Tres motivos son al parecer los más evidentes: internos y externos a la persona y la falta de motivación e interés por su trabajo.
... To date, research on CAB has mainly focused on the study of issues such as the rates of prevalence, the potential antecedents of CAB, or the effectiveness of deterrence strategies against this phenomenon (e.g., Whitley, 1998;McCabe, 2005;Malgwi and Rakovski, 2009;Teixeira and Rocha, 2010;Paulhus and Dubois, 2015;Sattler et al., 2017). Research has also examined the CAB-AP relationship. ...
... To date, research on CAB has mainly focused on the study of issues such as the rates of prevalence, the potential antecedents of CAB, or the effectiveness of deterrence strategies against this phenomenon (e.g., Whitley, 1998;McCabe, 2005;Malgwi and Rakovski, 2009;Teixeira and Rocha, 2010;Paulhus and Dubois, 2015;Sattler et al., 2017). Research has also examined the CAB-AP relationship. ...
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Counterproductive academic behaviors (CAB) are a complex phenomenon that affects academic institutions in multiple geographical areas with different cultures, values, and social norms. The high incidence of CAB causes problems of critical importance that transcend the educational domain. The current study aims to contribute to the knowledge of the CAB consequences by focusing on its impact on academic performance (AP). For this purpose, a meta-analysis was conducted in order to examine the relationship between CAB, its facets, and AP. The results show that overall CAB and students' performance are negatively related with a true effect size of ρ = −0.40 (K = 231, N = 127,269). Particularly, absenteeism appeared to be the facet most strongly related to AP (ρ = −0.48, K = 117, N = 69,453). A meta-analytic path analysis model was carried out in order to test the predictive validity of CAB, students' personality characteristics, and intelligence on AP. Results show that conscientiousness and cognitive intelligence have a negative relationship with CAB (β = −0.28 and β = −0.20, respectively), and that conscientiousness, openness to experience, intelligence, and CAB can explain 58% of AP true variance. Meta-analyses of moderator variables and hierarchical meta-analyses are also presented. The implications for research and practice are discussed at the end.
... Although there is empirical support to show that sanctions for cheating do act as a deterrent (Nagin and Pogarsky 2003;Whitley 1998), cheating has continued unabated for 40 years (DiPietro 2010). It has been suggested that faculty members prefer not to take formal actions against dishonest students (Fontana 2009), do not use methods available to them to detect cheating (Sattler et al. 2017) and respond to academic misconduct in inconsistent ways (Tennant et al. 2007). This may indicate that educators choose their own punitive and preventative methods, rather than following institutional policy, which could contribute to significant variation between educators (Baran and Jonason 2020;East 2009). ...
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In nursing, expectations of honesty and integrity are clearly stipulated throughout professional standards and codes of conduct, thus the concept of academic integrity has even more impetus in preparing students for graduate practice. However, a disparity between policy and practice misses the opportunity to instil the principles of academic integrity, and at its core honesty, a pivotal trait in the nursing profession. This study draws upon the experience of the nursing faculty to explore how academic integrity policy of deterrence operate in nursing education. While participants deplored cheating behaviours, they expressed frustration in having to ‘police’ large numbers of students who had little awareness of the academic standards to meet policy requirements. In addition, they were cynical because of a perceived lack of severity in sanctions for students who repeatedly breached integrity. Participants expressed a moral obligation as educators to meet student learning needs and preferred to engage with students in a more meaningful way to uphold academic integrity. The ambivalence to detect and report breaches in integrity undermines the effectiveness of policy. Therefore, faculty must recognise the importance of their role in detecting and escalating cases of dishonesty and execute deterrence in a more consistent way. To do this, greater support at an institutional level, such as smaller class sizes, inclusion in decision making around sanctions and recognition of additional workload, will enable faculty to uphold policy. Although policing was not their preferred approach, the role of faculty in detecting and reporting cases of misconduct is crucial to increase the certainty of students getting caught, which is essential if policy is to be effective in deterring dishonest behaviour.
... Of the two methods, promoting a culture of academic integrity is forward-looking while enforcement of rules is backward looking (McCabe & Katz, 2009;Minarcik & Bridges, 2015). Moreover, responsibility of creating a culture conducive to academic integrity rests entirely with the faculty but are shared with administrative staff in the lateral method (Sattler et al., 2017). 2 In a recent article, Ullah (2019) highlighted the failure of the public policy to curb academic dishonesty in institutes of higher learning in Pakistan. ...
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Academic frauds, dishonesty and cheating are pervasive in Pakistan, but thus far less systematic research has been undertaken on the effectiveness of the policies designed for countering academic dishonesty. Generally, the success of a policy depends on a good design and appropriate implementation. The design aspect of the policies to counter academic dishonesty in Pakistan is studied elsewhere, the purpose of this article is to empirically examine the implementation stages of the same policies. In doing so, the study utilizes a panel data set comprising of 60 cross-sections and 06 time series observations (2012–2017). Results of the rigorous panel regression models shows that leniency in the prescribed punishments and an exhaustive belief on the efficacy of traditional examination surveillance methods are counterproductive. The study however failed to substantiate the direct relationship of discrimination and academic dishonesty in the sample. It is therefore concluded that prescribed punishments must be awarded in letter and spirit and that there is need for coordination at different stages of the implementation. Further, the study also recommends that traditional forms of examination surveillance must be augmented using modern technology.
... When the content of a text is used in an undesirable way, particularly when the source is not cited, plagiarism may occur (Sattler et al. 2017;Leung and Cheng 2017). Plagiarism is an age old issue and is only more problematic because of the ease with which text can be reused and disguised due to the widespread use of computers and the ubiquitous presence of the Internet. ...
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Plagiarism is a common problem in the modern age. With the advance of Internet, it is more and more convenient to access other people’s writings or publications. When someone uses the content of a text in an undesirable way, plagiarism may occur. Plagiarism infringes the intellectual property rights, so it is a serious problem nowadays. However, detecting plagiarism effectively is a challenging work. Traditional methods, like vector space model or bag-of-words, are short of providing a good solution due to the incapability of handling the semantics of words satisfactorily. In this paper, we propose a new method for plagiarism detection. We use Word2vec to transform the words into word vectors which are able to reveal the semantic relationship among different words. Through word vectors, words are clustered into concepts. Then documents and their paragraphs are represented in terms of concepts, and plagiarism detection can be done more effectively. A number of experiments are conducted to demonstrate the good performance of our proposed method.
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Introduction. The article presents a review of psychological studies aimed at analyzing digital academic dishonesty (academic misconduct with the use of the Internet). The socio-psychological specifics of digital academic dishonesty, which distinguishes it from traditional forms of academic dishonesty, have been studied quite fragmentally to date. The purpose of this study is to identify socio-psychological factors that determine the involvement of students in digital academic dishonesty in terms of digitalization of education. Materials and Methods. In order to achieve the research goal, we used the method of systematic review of research articles published in 1995-2021 and indexed in the databases ‘Scopus’ and ‘Web of Science’. According to the criteria (relevant keywords; availability of a detailed description of the research program, empirical results; Russian or English), 55 articles were included in the final array of analysis. Results. We identified individual-psychological and contextual-environmental factors of digital academic dishonesty. Individual psychological factors include: students’ attitudes towards digital academic dishonesty; students’ academic experience; students’ personal characteristics; socio-demographic characteristics of students. Contextual and environmental factors include: students’ attitude to the prevalence of digital academic dishonesty among peers; teachers’ attitude to digital academic dishonesty; institutional policy on digital academic dishonesty. Conclusions. The authors conclude that the socio-psychological risk factors for involving schoolchildren and students in digital academic dishonesty are their previous experience of academic dishonesty, the idea of the acceptability of this form of academic behavior, the lack of educational motivation and self-regulation, insufficient level of knowledge and information competence (individual psychological factors), as well as the prevalence of digital academic dishonesty in an educational institution with the background of the lack of an institutional policy to prevent digital academic dishonesty and relevant actions of teachers (contextual and environmental factors).
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La Deshonestidad Académica (DA) es una de las grandes dificultades que está teniendo la universidad en la formación de futuros profesionales pues es uno de los problemas éticos que más se está acrecentando en la actualidad. Debido a la ausencia de instrumentos con propiedades psicométricas adecuadas para evaluar la DA, el presente trabajo tiene como objetivo desarrollar y analizar las propiedades de medida de un test que mide la percepción de los alumnos frente a acciones que implican DA en el ámbito universitario, llamado Percepciones hacia la Deshonestidad Académica (PDA). En los resultados se constata que el PDA tiene una fiabilidad apropiada. Se presentan evidencias de validez de constructo de las puntuaciones en una amplia muestra de estudiantes universitarios españoles. Contar con un instrumento de medida fiable y con evidencias de validez puede servir a los investigadores del área para diseñar propuestas de intervención que fortalezcan la integridad académica a nivel universitario.
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The paper reviews the literature and dwells upon the reasons behind the occurrences of plagiarism. It reiterates that the anti-plagiarism software are automated programs and should be used in conjunction with human intelligence and detailed human scrutiny. The authors highlights the advantages and disadvantages of using the anti-plagiarism software and recommend that training sessions and orientation programs be organized for the students and researchers to sensitize them to the basic principle of honesty in academic and research enterprise, impacting all the stakeholders.
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An e-mail survey of 373 faculty members at six colleges and universities in the United States, Israel, and Germany revealed that student academic dishonesty (AD) is problematic at their institutions. Professors followed institutional policy but exercised discretion in handling specific cases of AD. They also engaged in varying levels of discussion, written communication, and actions as part of the hidden curriculum designed to address the problem of student dishonesty. Eleven scenarios of AD were posed and faculty indicated the sanction they thought was appropriate for the involved student. Repeat offenders were given the most punitive sanctions. Factor analysis revealed that the scenarios had three underlying constructs, one of which related to the use of data from another student or class. On this factor faculty from the U.S. and Germany had more tolerant attitudes toward AD, sanctioning students less severely, than those from Israel. Policy implications of these findings are addressed.
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This study investigates the opinions of faculty members on the subject of academic integrity at two state universities in the western United States. Results show strong similarities for both universities. Most faculty members are lenient to first offenders and would counsel students after plagiarism is discovered. However, as a group they do little to promote student awareness on what constitutes academic dishonesty. Further, although most faculty members are more stringent in their reactions to second time offenders, most admit there is no tracking of such activities in their departments or schools. The faculties believe that student academic integrity is a problem at both schools.
A significant amount of research has been undertaken in response to high levels of student plagiarism in higher education institutions (HEI). New models have emerged over the last decade for strategies and systems for detection, penalties and mitigation, based on deeper understanding of the underlying reasons behind student plagiarism. Most research has been initiated by academics from English speaking countries, particularly from the UK, North America and Australia. When the proposal for the Impact of Policies for Plagiarism in Higher Education across Europe (IPPHEAE ) project was developed during 2009 very little research had been conducted about the policies for academic integrity adopted by HEIs in the majority of countries in Europe. IPPHEAE, funded by the European Commission (2010–2013), included a comparative study of policies and procedures in place in HEIs across 27 European Union (EU) member states for handling aspects of academic integrity, focusing specifically on bachelor and master’s levels. The survey instruments were on online questionnaires, student focus groups, structured interviews and analysis of documentary evidence, designed with a view to capture a range of quantitative and qualitative responses from different perspectives. Almost 5,000 responses were captured for the survey, mainly from online questionnaires, made available in 14 languages. Different questions were asked of students, teaching staff and senior managers, to determine how well institutional procedures were understood, to what extent they were operating as intended and whether there was consistency of outcomes within and between institutions. Interviews with researchers and people associated with national bodies and agencies responsible for higher education (HE) quality or academic integrity explored broader perspectives on issues such as national policies and how responses to plagiarism aligned with policies for quality and standards. This paper presents results from the survey that focus specifically on institutional policies, highlighting examples of good practice and also areas of concern. The findings suggest that different approaches should be adopted according to the maturity of existing policies and systems in all the countries surveyed, to promote more effective assurance of quality, standards and academic integrity.
This paper considers the problem of managing the workload implications of plagiarism detection as part of the larger issue of assessment management and within a holistic approach to educational integrity. It looks specifically at the potential for Electronic Assessment Management (EAM) to provide some of the solutions to this problem. It draws on the work of Mantz Yorke whose research into assessment management calls for the establishment of appropriate structures and mechanisms which support systems that achieve the dual imperatives of efficiency and effectiveness. This paper considers the workload issues related to plagiarism detection under these dual imperatives, looking first at the issue of effectiveness and then turning to consider the issue of efficiency. Finally, it argues for why and how these issues should be taken into account in the procurement of digital plagiarism detection software and how the use of these tools should fit within a rigorous and consistent holistic approach to educational integrity.
The incidence of plagiarism, according to the literature, is increasing. But why do students plagiarise and why the increase? Is it due to laziness, opportunity, ignorance, fear or ambivalence? Or do they know that there is little chance of any significant penalty? The literature suggests that all of these apply. Given this, are universities and, by implication, staff, rather than students culpable for such attitudes and are they guilty for the "soft" consequences? This paper addresses the question of student and staff attitudes towards plagiarism and suggests that if the teaching faculty view plagiarism as a serious problem, they have an obligation to actively change student attitudes by demanding system wide support. The authors argue that exhorting students not to plagiarise and appealing to their moral compass are not sufficient to reduce the frequency of plagiarism and neither are these enough to change their attitudes. Instead, active education is required leading to a situation whereby students are taught, in the most practical sense, the skills expected of them when submitting academic writing. Equally, staff need adequate understanding of what might be happening when plagiarism occurs, and to be able to address the issue consistently in a supported, non-threatening institutional environment. To achieve this, a gradual release model is proposed as a path to a convergent approach to plagiarism.
Incidents of academic dishonesty continue to affect every college and university in the nation, at both the undergraduate and graduate levels. At some point during their academic careers, estimates are that 50-70% of all college students engage in cheating, plagiarism and other forms of dishonesty. The need for action to minimize this problem is evident, especially given the need of employers for highly-skilled and ethical workers in a global economy, and the recent spate of business scandals related to ethical misconduct. This paper describes what various colleges and universities are doing to combat the problem. This usually involves the creation of specific policies, a committee to enforce those policies, and a mechanism to communicate them to students and faculty. Institutions use different approaches to address the problem, but all have the same goal - to reduce and hopefully eliminate unethical behavior by students in their academic endeavors.