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Review article, academic integrity in online assessment: a research review.

• Department of Psychology, Queen’s University, Kingston, ON, Canada

This paper provides a review of current research on academic integrity in higher education, with a focus on its application to assessment practices in online courses. Understanding the types and causes of academic dishonesty can inform the suite of methods that might be used to most effectively promote academic integrity. Thus, the paper first addresses the question of why students engage in academically dishonest behaviours. Then, a review of current methods to reduce academically dishonest behaviours is presented. Acknowledging the increasing use of online courses within the postsecondary curriculum, it is our hope that this review will aid instructors and administrators in their decision-making process regarding online evaluations and encourage future study that will form the foundation of evidence-based practices.

Introduction

Academic integrity entails commitment to the fundamental values of honesty, trust, fairness, respect, responsibility, and courage ( Fishman, 2014 ). From these values, ethical academic behavior is defined, creating a community dedicated to learning and the exchange of ideas. For a post-secondary institution, ensuring that students and staff are acting in an academically integrous manner reinforces an institution's reputation such that an academic transcript, degree, or certificate has a commonly understood meaning, and certain knowledge and skills can be inferred of its holder. In turn, individual students benefit from this reputation and from the inferences made based on their academic accomplishments. At a broader level, understanding the fundamental values of academic integrity that are held within a community—and behaving in accordance with them—instills a shared framework for professional work, making explicit the value of the mastery of knowledge, skills, and abilities.

Fair and effective methods for promoting academic integrity have long been considered within postsecondary education. Yet, there is a widespread belief that departures from integrity are on the rise (e.g. Hard et al., 2006 ). With the introduction of technology into the classroom and the popularity of online classes, new opportunities for “e-cheating” exist (e.g. Harmon and Lambrinos, 2008 ; King and Case, 2014 ). Demonstrating the importance of considering “e-cheating,” prior to 2020, reports suggest that 30% of students in degree-granting U.S. colleges and universities enrolled in at least one online course ( Allen and Seaman, 2017 ), and 44% of faculty respondents reported teaching at least one fully online course ( Jaschik and Lederman, 2018 ). In 2020 and as of this writing, the COVID-19 pandemic has caused widespread changes to higher education, resulting in many institutions adopting online learning formats. As the development of fully online courses is expected to continue to expand (e.g., Allen and Seaman, 2010 ; Johnson, 2019 ), faculty and administrators are faced with the challenge of developing methods to adequately assess student learning in an online environment while maintaining academic honesty.

There are many new ways to cheat, some that are unique to the online course environment and some that are also observed within in-person courses; these include but are not limited to: downloading papers from the internet and claiming them as one’s own work, using materials without permission during an online exam, communicating with other students through the internet to obtain answers, or having another person complete an online exam or assignment rather than the student who is submitting the work ( Jung and Yeom, 2009 ; Moten et al., 2013 ; Rogers, 2006 ; Underwood and Szabo, 2003 ). In particular, both faculty and students perceive online testing to offer more cheating opportunities than in traditional, live-proctored classroom environments ( Kennedy et al., 2000 ; Rogers, 2006 ; Stuber-McEwen et al., 2005 , Smith, 2005 ; Mecum, 2006 ), with the main concerns being student collaboration and use of forbidden resources during the exam ( Christe, 2003 ).

The goal of this paper is to review and synthesize current research on academic integrity in higher education, considering its specific application to assessment practices in online education. Understanding the varied and complex types and causes of academic dishonesty can inform the suite of methods that might be used to most effectively promote academic integrity. Thus, we will address the question of why students engage in academically dishonest behaviours ( Why do Students Engage in Academic Dishonesty? ), and we will review methods to reduce academically dishonest behaviours (Section 3). We will do this with intentional consideration of four factors: individual factors, institutional factors, medium-related factors, and assessment-specific factors. Given the increasing use of online courses within the postsecondary curriculum, it is our hope that this review will aid instructors and administrators in their decision-making process regarding online evaluations and encourage future study that will form the foundation of evidence-based practices 1 .

Why do Students Engage in Academic Dishonesty?

Academic dishonesty (or “cheating”) 2 includes behaviors such as the use of unauthorized materials, facilitation (helping others to engage in cheating), falsification (misrepresentation of self), and plagiarism (claiming another’s work as one’s own; e.g., Akbulut et al., 2008 ; Şendağ et al., 2012 ), providing an unearned advantage over other students ( Hylton et al., 2016 ). Broadly, these behaviors are not consistent with an established University’s Standards of Conduct ( Hylton et al., 2016 ), which communicates expected standards of behavior ( Kitahara and Westfall, 2007 ). “E-dishonesty” has been used to refer to behaviors that depart from academic integrity in the online environment, and e-dishonesty raises new considerations that may not have been previously considered by instructors and administrators. For example, concerns in relation to online exams typically include ‘electronic warfare’ (tampering with the laptop or test management system), impersonation, test item leakage, and the use of unauthorized resources such as searching the internet, communicating with others over a messaging system, purchasing answers from others, accessing local/external storage on their computer, or accessing a book or notes directly (e.g. Frankl et al., 2012 ; Moten et al., 2013 ; Wahid et al., 2015 ). All of these types of behaviours are also considered under the broader umbrella term of ‘academic dishonesty’ ( Akbulut et al., 2008 ; Namlu and Odabasi, 2007 ), and we highlight them here to broaden the scope of considerations with respect to academic integrity.

There are many reasons why individuals may choose to depart from academic integrity. Here, we synthesize existing research with consideration of individual factors, institutional factors, medium-related factors, and assessment-specific factors. Much of the research to date considers the on-campus, in-person instructional context, and we note the applicability of much of this literature to online education. Where appropriate, we also note where research is lacking, with the aim of encouraging further study.

Individual Factors

Research based on what is referred to as the “fraud triangle” proposes that in order for cheating to occur, three conditions must be present: 1) opportunity, 2) incentive, pressure, or need, and 3) rationalization or attitude (e.g. Becker et al.2006 ; Ramos, 2003 ). These three conditions are all positive predictive factors of student cheating behavior ( Becker et al., 2006 ). Opportunity occurs when students perceive that there is the ability to cheat without being caught; this perception can occur, for example, if instructors and administrators are thought to be overlooking obvious cheating or if students see others cheat or are given answers from other students ( Ramos, 2003 ). The second condition, incentive, pressure, or need , can come from a variety of different sources such as the self, parents, peers, employers, and universities. The pressure felt by students to get good grades and the desire to be viewed as successful can create the incentive to cheat. Lastly, the rationalization of cheating behavior can occur when students view cheating as consistent with their personal ethics and believe that their behavior is within the bounds of acceptable conduct ( Becker et al., 2006 ; Ramos, 2003 ). Similar to the “cheating culture” account (detailed more fully in Institutional Factors ), rationalization can occur if students believe that other students are cheating, perceive unfair competition, or perceive an acceptance of, or indifference to, these behaviors by instructors ( Varble, 2014 ).

Though accounts based on the fraud triangle are well supported, other researchers have taken a more fine-grained approach, further considering the second condition related to incentive, pressure, or need . Akbulut et al. (2008) , for example, propose that psychological factors are the most significant factors leading students to e-dishonesty. Feeling incompetent and/or not appreciating the quality of personal works or one’s level of mastery ( Jordan, 2001 ; Warnken, 2004 ; Whitaker, 1993 ), a sense of time pressure ( DeVoss and Rosati, 2002 ; Sterngold, 2004 ), a busy social life ( Crown and Spiller, 1998 ), personal attitudes toward cheating ( Diekhoff et al., 1996 ; Jordan, 2001 ), and the desire to get higher grades ( Antion and Michael, 1983 ; Crown and Spiller, 1998 ) can cause an increase in academic dishonesty, including e-dishonesty.

In particular relation to online courses, some authors contend that the online medium may serve as a deterrent for academic dishonesty because it often supports a flexible schedule and does not lend itself to panic cheating ( Grijalva et al., 2006 ; Stuber-McEwen et al., 2009 ). Indeed, often, a reason why students enroll in online courses is the ease and convenience of an online format. However, if students become over-extended, they may use inappropriate resources and strategies to manage (e.g. Sterngold, 2004 ). In addition, the isolation that students may experience in an online course environment can also increase stress levels and lead them to be more prone to dishonest behaviors ( Gibbons et al., 2002 ).

Institutional Factors

Individual students are part of larger university culture. By some accounts, a primary contributor to academic dishonesty is the existence of a “cheating culture” ( Tolman, 2017 ). If a university has an established culture of cheating—or at least the perception of a culture of cheating—students may be tolerant of cheating, believe that cheating is necessary in order to succeed, and believe that all students are cheating ( Crittenden et al., 2009 ). Students directly shape cheating culture, and thus subsets of students in a university population may have their own cheating cultures ( Tolman, 2017 ). It is plausible, then, for online students to have their own cheating culture that differs from the rest of the student population. However, if this subset of students is identified as being at risk for academic dishonesty, there is the opportunity for the university to proactively address academic integrity in that student group ( Tolman, 2017 ).

We note, however, the peculiar situation of current the COVID-19 pandemic, particularly for universities that transitioned to mostly online courses. A university’s cheating culture may change, as large numbers of students may be faced with increased pressures and as online courses are designed—and assessments developed—with atypical rapidity. It will be necessary for future research on university cheating culture, both on campus and online, to consider the potential long-term impacts of the pandemic on “appropriate” student behaviors. For example, there appear to be many new opportunities for students to share papers and coursework with peers in online forums. In some cases this sharing may be appropriate, whereas in others it may not. Determining effective methods for communication of boundaries related to academic honesty—especially when boundaries can vary depending on the nature of an assignment—will be especially important.

Institutional policies related to the academic standards of the university also impact academic honesty on campus. Some institutional policies may be too lax, with insufficient sanctions and penalization of academic dishonesty (e.g., Akbulut et al., 2008 ). Further, even when sanctions and penalization are adequate, a lack of knowledge of these policies within staff, administrators, and students—or insufficient effort made to inform students about these policies—can result in academic dishonesty (see also Jordan, 2001 ). For example, McCabe et al. (2002) found a significant correlation such that academic dishonesty decreased as students’ and staff’s perceived understanding and acceptance of academic integrity policies increased. Additionally, academic dishonesty was found to be inversely related to the perceived certainty of being reported for academic dishonesty and the perceived severity of the university’s penalties for academically dishonest behavior. Relatedly, universities with clear honor codes had lower academic dishonesty than universities without honor codes ( McCabe et al., 2002 ). Given these findings, universities should make academic conduct policies widely known and consider implementing honor codes to minimize the cheating culture(s). Specifically, for online courses, these findings suggest that the university’s academic conduct policies and honor codes should be directly stated on course sites.

Medium of Delivery

The belief that cheating occurs more often in online courses than in in-person courses—particularly for high-stakes assessments like exams—is widespread, with approximately 42–74% of students believing it to be easier to cheat in an online class ( King et al., 2009 ; Watson and Sottile, 2010 ). Thus, the question of whether students are cheating at greater rates in online classes is paramount in evaluating the reliability of online assessments as measurements of mastery in higher education. Though there have been many studies of academic dishonesty in in-person classes, few studies have attempted to compare cheating rates between in-person and online classes. In those that do, the results appear to be inconsistent with some studies demonstrating that cheating occurs more often in online classes than in in-person classes ( Lanier, 2006 ; Khan and Balasubramanian, 2012 ; King and Case, 2014 ; Watson and Sottile, 2010 ), others demonstrating equivalent rates of cheating ( Grijalva et al., 2006 ; Ladyshewsky, 2015 ), and some demonstrating that cheating occurs more often in-person ( Stuber-McEwen et al., 2009 ). Table 1 provides a summary of these studies, and we highlight some of them below.

TABLE 1 . Studies Comparing Academic Dishonesty in Online Classes and In-Person Classes .

Four studies to date have found cheating rates to be higher in online courses than in in-person courses. Lanier (2006) , for example, surveyed college students ( n = 1,262) in criminal studies and legal studies courses and found that 41.1% of respondents admitted to cheating in an online course while 21.3% admitted to cheating in an in-person course. The study also found some preliminary evidence for differences in cheating rates between majors, though the sample sizes for some groups were too low to be reliable: business majors were the most likely to cheat ( n = 6, 47.1%), followed by “hard sciences” ( n = 20, 42.6%), and “social sciences” ( n = 282, 30%). Though clearly tentative given the small sample sizes, these data suggest that there may be different cheating cultures that exist within universities, demonstrating the importance of considering group-level culture differences with respect to cheating ( Institutional Factors above). Further supporting an increased rate of cheating in online assessments, Khan and Balasubramanian (2012) surveyed undergraduate students attending universities in the United Arab Emirates ( N = 224) and found that students admitted to higher cheating rates using technology or e-cheating. Although this study did not differentiate between online and in-person course formats, it does suggest an increase in cheating via the use of online technology.

Using the Student Ethical Behavior instrument with undergraduate students enrolled in a business course ( n = 1867), King and Case (2014) found higher cheating rates in online exams than in in person-exams. Specifically, researchers found that 15% of students admitted to cheating on an in-person exam, at about 2.9 times a semester, while 29% admitted to cheating on an online exam, at about 3.3 times a semester. Thus, not only were students cheating at higher rates in online exams as compared to in-person exams, but those that did admit to cheating were also cheating more frequently during a semester. Consistent with this finding, Varble (2014) analyzed the test scores of students enrolled in an online or an in-person, undergraduate marketing course. Students took exams either online or in person. The study found higher mean test scores in the online test group with the exception of one test, than test scores in the in-person test group. The difference in scores was largely attributed to “remember” type questions which rely on a student’s ability to recall an answer, or alternatively, questions which could be looked up in unauthorized resources. Given these findings, Varble (2014) concluded that cheating may have taken place more often in the online tests than in-person tests.

In contrast to studies reporting increases in academic dishonesty in online assessments, other studies have found lower rates of cheating in online settings as compared to in-person. Grijalva et al. (2006) used a randomized response survey method with 725 undergraduate students taking an online course and estimated that only 3–4% of students cheated. Consistent with this finding, Stuber-McEwen et al. (2009) surveyed in-person ( n = 225) and online students ( n = 138) using the Student Academic Dishonesty Survey and found that online students reported engaging in less academic misconduct than in-person students. An important methodological feature to consider, however, is that the sample of online students consisted of more mature distance study learners than the in-person sample; this study may not be applicable to the general university population. 3

Although some studies have found cheating rates to be higher or lower in online classes, some have not found significant differences. Watson and Sottile (2010) , for example, used the Academic Dishonesty Assessment with a sample of undergraduate and graduate students from different faculties ( n = 635). The study found that 32.7% of respondents admitted to cheating in an online course while 32.1% admitted to cheating in an in-person course. However, the data also demonstrated that students were significantly more likely to cheat by obtaining answers from others during an online quiz or test than in an in-person quiz or test (23.2–18.1%) suggesting that students in an online course tended to cheat more in an online exam, while students in an in-person course tended to cheat through other assignments. Additionally, students admitted that they were four times more likely to cheat in an online class in the future compared to an in-class format (42.1–10.2%) ( Watson and Sottile, 2010 ). This study points to a potential importance of addressing cheating particularly in online exams in order to ensure academic honesty in online courses, though we note that Ladyschewsky (2015) found that in a sample of post-graduate students ( n = 136), multiple-choice test scores in an unproctored online format were not different from scores from a proctored, in-person exam.

Assessment-specific Factors

Research varies in the context in which cheating is explored. For example, some studies examine cheating within some or all types of online assessments, whereas others specifically focus on exams. The type of assessment likely matters when it comes to academic behaviors. For example, though Lanier (2006) found higher reporting of cheating in online courses, the study did not distinguish among assessments, and instead focused on cheating across all assignments in classes. Yet, in Watson and Sottile (2010) ; described above, students were significantly more likely to cheat by obtaining answers from others during an online quiz or test than in an in-person quiz or test (23.2–18.1%), suggesting that students in an online course tended to cheat more in an online exam, while students in an in-person course tended to cheat through other assignments.

If students are more likely to engage in academic dishonesty on high-stakes summative assessments (e.g., exams) rather than formative assessments throughout the term—and if online exams offer more opportunities for dishonesty—then rates of cheating would be expected to differ depending on the assessment type. Additionally, when comparing dishonesty in online and on-campus courses, the differences might be minimal in relation to assessments that allow for plagiarism (e.g., essays that may be completed “open book” over an extended time period; e.g., Watson & Sottile, 2010 ). We thus encourage future research to consider the type of assessment when comparing cheating in online vs. in-person course environments.

Methods for Reducing Academic Dishonesty in Online Assessment

Just as the reasons for why students cheat are varied, so too are methods for reducing academic dishonesty. We again organize the topic in relation to factors related to the individual student, the institution, the medium of delivery, and the assessments themselves. Throughout, we focus primarily on summative assessments that may have various formats, from multiple-choice questions to take-home open-book essays. Though the methods for preventing cheating are discussed separately from the reasons why students cheat in this paper, we emphasize that the methods must be considered in concert with consideration of the reasons and motivations that students may engage in academic dishonesty in the first place.

Individual- and Institutional-Level Methods

We discuss both individual- and university-level methods to reduce academic dishonesty together here, as current methods consider the bi-directional influence of each level. As highlighted in Institutional Factors , institutional factors that can increase academic dishonesty include lax or insufficient penalization of academic dishonesty, insufficient knowledge of policies and standards across students, instructors, and administrators, and insufficient efforts to inform students about these policies and standards ( Akbulut et al., 2008 ; Jordan, 2001 ). In order to ensure academic honesty at universities, administrators and staff must clearly define academic dishonesty and what behaviors are considered academically dishonest. Students often demonstrate confusion about what constitutes academic dishonesty, and without a clear definition, many students may cheat without considering their behaviors to be academically dishonest. Thus, the more faculty members discuss academic honesty, the less ambiguity students will have when confronting instances of academic dishonesty ( Tatum and Schwartz, 2017 ). In addition to making students aware of what constitutes academic dishonesty, it is also important to make students aware of the penalties that exist for academically dishonest behavior. Academic dishonesty is inversely related to the perceived severity of the university’s penalties for academically dishonest behavior ( McCabe et al., 2002 ). When faculty members are aware of their institutions policies against academic dishonesty and address all instances of dishonesty, fewer academically dishonest behaviors occur ( Boehm et al., 2009 ).

Faculty and staff can influence the cheating culture of their university simply by discussing the importance of academic honesty with their students. These discussions can help shape and change a student’s beliefs on cheating, hopefully reducing their ability to rationalize academically dishonest behavior. Discussions with students on the importance of academic honesty may help reduce feelings of overestimated cheating frequency among peers, and may prevent students from rationalizing cheating behavior. Honor codes, for example, are effective at reducing academic dishonesty when they clearly identify ethical and unethical behavior ( Jordan, 2001 ; McCabe and Trevino, 1993 ; McCabe and Trevino, 1996 ; McCabe et al., 2001 ; McCabe et al., 2002 ; Schwartz et al., 2013 ), and are associated with perceptions of lower cheating rates among peers ( Arnold et al., 2007 ; Tatum and Schwartz, 2017 ). Further, these codes reduce students’ ability to rationalize cheating ( Rettinger and Kramer, 2009 ), increase the likelihood that faculty members and students will report violations ( Arnold et al., 2007 ; McCabe and Trevino, 1993 ), and increase the perceived severity of sanctions ( McCabe and Trevino, 1993 ; Schwartz et al., 2013 ). In addition to implementing honor codes school-wide, honor codes can also be implemented into specific courses and have been shown to reduce cheating and improve communication between students and faculty by increasing feelings of trust and respect among the students ( Konheim-Kalkstein, 2006 ; Konheim-Kalkstein et al., 2008 ).

Methods in Relation to the Medium of Delivery

Multiple methods to combat academic dishonesty in online assessments focus on the manner in which the assessment is delivered and invigilated. One view is that an in-person proctored, summative exam at a testing center is the best practice for an otherwise online course because of the potential ease of cheating in an unproctored environment or an online-proctored environment ( Edling, 2000 ; Rovai, 2000 ; Deal, 2002 ). Another view is that with the correct modifications and security measures, online exams offer a practical solution for students living far from campus or other testing facilities while still maintaining academic integrity. However, both proposed solutions come with their own disadvantages. Requiring students to travel to specific exam sites may not be feasible for remote students, and hiring remote proctors can be expensive ( Rosen and Carr, 2013 ). Indeed, in the current context of the global COVID-19 pandemic, in-person proctoring has been unfeasible in many regions.

In Methods in Relation to the Medium of Delivery , we focus on methods that do not require in-person proctoring. The assessment type we focus on is the summative exam, though we note the variability in the style that such assessments can take. There are currently various means of detecting cheating in online exams, and we have chosen to discuss these means of detection separately from methods use to prevent cheating as the implementation tends to occur at a different level and for a different purpose (e.g., technological systems that detect cheating while it is occurring or shortly after, rather than solutions at the level of assessment format that are designed to promote academic integrity). However, we do note that if students are aware of the cheating detection systems in place, the systems may have a preventative effect.

Online Cheating Detection

The exam cheating detection systems described below have been developed, in part, because holding exams in-person at a registered location with live proctors is often not feasible due to financial, travel, or other logistical reasons ( Cluskey et al., 2011 ). The general types of online proctoring include video summarization, web video recording, and live online proctoring; each is described below and in Figure 1 .

FIGURE 1 . Four types of proctoring: (A) in-person, (B) video summarization, (C) web video recording, and (D) live online proctoring.

Though online proctoring provides some intuitive advantages for detecting cheating behaviours, and it maps closely onto familiar face-to-face proctoring processes, many have raised concerns in media outlets about both the ethics and efficacy of these systems. For example, concerns have been raised about student invasion of privacy and data protection (e.g., Dimeo, 2017 ; Lawson, 2020 ), and breeches have occurred (e.g., Lupton, 2020 ). In addition to concerns related to privacy, cases have been reported where students were discriminated against by a proctoring software as a result of their skin colour (e.g., Swauger, 2020 ). Not only are there concerns about ethics regarding online proctoring software, but there are also concerns about whether these methods are even effective, and if so, for how long. For example, there have long been readily available guides that demonstrate how to “cheat” the cheating software (e.g., Binstein, 2015 ). If an instructor deems online proctoring effective and necessary, prior to using online proctoring, instructors should explicitly consider whether students are treated justly and equitably, just as they should in any interaction with students. Instructors are also encouraged to carefully investigate privacy policies associated with online cheating detection software, and any applicable institution policies (e.g., data access and retention policies), prior to using such technology.

Video Summation

Video summarization software, also referred to as video abstraction , utilizes artificial intelligence to detect cheating events that may occur during the exam ( Truong and Venkatesh, 2007 ). Students are video recorded using their own webcam throughout the exam. If a cheating event is detected, the program will flag the video for future viewing by a proctor. Thus, the time demands of proctors are reduced, yet students are monitored. Video summarization programs can generate either keyframes (a collection of images extracted from the video source) or video skims (video segments extracted from the video source) to represent potential cheating behavior (e.g. Truong and Venkatesh, 2007 ). Both of these forms convey the potential cheating event in order for future determination by a human proctor. However, video skims have an advantage over keyframes in that they have an ability to include audio and motion elements which convey pertinent information in the process of invigilation ( Cote et al., 2016 ).

The main advantage of choosing an invigilation service like this one is that it reduces the hours that proctors must put in into invigilating the exam. However, detecting cheating behavior without live human interaction is a difficult process. Modeling suspicious behavior is complex in that cheating behavior does not typically follow a pattern or type, thus making it difficult to recognize accurately ( Cote et al., 2016 ). Therefore, some suspicious activity may not be detected, and administrators may not be able to guarantee that all cheating behavior has been deterred or detected. Further, there is no opportunity for a live proctor to intervene or gather more information if atypical behaviour is occurring, limiting the ability to mitigate a violation of academic integrity if it is occurring, or about to occur.

Web Video Recording

In relation to online exams, web video recording refers to situations in which the student is video recorded throughout the entirety of the exam for later viewing by an instructor. Like video summarization methods, detection software can be used in order to flag any suspicious activity for later viewing. Administrators and instructors may feel more confident in this service as they can view the entire exam, not only the flagged instances. However, reviewing all exams individually may not be feasible, and most exams are not reviewed in full. Unlike video summarization programs, web video recording programs do not have specific proctors review all flagged instances, and instead rely on review by the administrators and instructors themselves. Knowing that the recording is occurring may deter students, but as with detection based on artificial intelligence, it is not guaranteed that all cheating behavior will be detected. It is important to note that with this method, as with the previous method, there is no opportunity for intervention by a proctor if an event is flagged as a possible violation of academic integrity. Thus, there may be ambiguous situations that have been flagged electronically with no opportunity to further investigate, and missed opportunities for prevention.

Live Online Proctoring

The final type of online proctoring, and arguably the most rigorous, is referred to as live online proctoring or web video conference invigilation . This method uses the student’s webcam and microphone to allow a live-proctor to supervise students during an online exam. Services can range from one-on-one invigilation sessions to group invigilation sessions where one proctor is supervising many students. Many administrators may feel the most comfortable using this kind of service as it is closest to an in-person invigilated exam. However, even with a live proctor supervising the student(s), cheating behavior can go undetected. At the beginning of a session, students are typically required to show their testing environment to their proctor; however, cheating materials can be pulled out during an exam unnoticed in the surrounding environment. If the proctor does not suspect cheating behaviors, they will not request another view of the entire room. Live online proctoring is also typically the most expensive of the options.

Online Cheating Detection: Other Solutions

Though online proctoring is one method for cheating detection, others also exist. Just as with online proctoring, instructors are encouraged to understand all applicable policies prior to using detection methods. Challenge questions, biometrics, checks for text originality, and lockdown browsers, are currently available technological options that instructors and institutions might consider.

Challenge Questions

Challenge or security questions are one of the simplest methods for authenticating the test taker. This method requires personal knowledge to authenticate the student and is referred to as a ‘knowledge-based authentication’ method ( Ullah et al., 2012 ). Students are asked multiple choice questions based on their personal history, such as information about their past home addresses, name of their high school, or mother(s) maiden name ( Barnes and Paris, 2013 ). Students must answer these questions in order to access the exam, and the questions may also be asked randomly during the assessment ( Barnes and Paris, 2013 ). These questions are often based on third-party data using data mining systems ( Barnes and Paris, 2013 ; Cote et al., 2016 ) or can be entered by a student on initial log-in before any examination. When a student requests an examination, the challenge questions are generated randomly from the initial profile set-up questions or third-party information, and answers are compared in order to verify the student’s identity ( Ullah et al., 2012 ). This relatively simple method can be used for authenticating the test taker; however, it cannot be used to monitor student behavior during the exam. Additionally, students may still be able to bypass the authentication process by providing answers to others to have another person take the exam, or to collaborate with others while taking the test. Thus, if chosen, this method should be used in concert with other test security methods in order to ensure academic honesty.

The use of biometrics, the measurement of physiological or behavioral features of an individual, is an authentication method that allows for continuous identity verification ( Baca and Rabuzin, 2005 ; Cote et al., 2016 ). This method of authentication compares a registered biometric sample against the newly captured biometrics in order to identify the student ( Podio and Dunn, 2001 ). When considering the use of biometric data, potential bias in identification, data security, and privacy must be carefully considered. It may be that the risks associated with the use of biometric data, given the intimate nature of these data, outweigh the benefits for an assessment.

There are two main types of biometric features: those that require direct physical contact with a scanner, such as a fingerprint, and those that do not require physical contact with a scanner such as hair color ( Rabuzin et al., 2006 ). Biometrics commonly use “soft” traits such as height, weight, age, gender, and ethnicity, physiological characteristics such as eyes, and face, and behavior characteristics such as keystroke dynamics, mouse movement, and signature ( Cerimagic and Rabiul Hasan, 2019 ). Combining two or more of the above characteristics improves the recognition accurateness of the program and is necessary to ensure security ( Cerimagic and Rabiul Hasan, 2019 ; Rabuzin et al., 2006 ).

Biometric-based identification is often preferred over other methods because a biometric feature cannot be faked, forgotten, or lost, unlike passwords and identification cards ( Prabhakar and Jain, 2002 ; Rudrapal et al., 2012 ). However, the biometric features that are considered should be universal, unique, permanent, measurable, accurate, and acceptable ( Frischholz and Dieckmann, 2000 ). Specifically, ideal biometric features should be permanent and inalterable, and the procedure of gathering features must be inconspicuous and conducted by devices requiring little to no contact. Further, the systems are ideally automated, highly accurate, and operate in real time ( Jain et al., 1999 ). However, no biometric feature to date meets all of the above criteria to be considered ideal, thus, it is important to measure multiple features in order to get the most accurate verification of identity (see Rabuzin et al., 2006 for an overview of all biometric features). Multimodal biometric systems use several biometric traits and technologies at the same time in order to verify the identity of the user ( Rabuzin et al., 2006 ). The multimodal system tends to be more accurate, as combining two or more features improves recognition accurateness ( Cerimagic and Rabiul Hasan, 2019 ).

Fingerprint recognition is one of the most broadly used biometric features as it is a unique identifier ( Aggarwal et al., 2008 ) and has a history of use in many different professional fields, most notably by the police. Additionally, fingerprints have become a commonly used identifier for personal handheld devices like phones. However, the use of fingerprint biometrics for student identification during online examinations can require additional resources such as fingerprint scanners, cellphones equipped with fingerprint technology, or other software at the student’s location, which may limit its current practicality ( Ullah et al., 2012 ). Similarly, face recognition uses image recognition and pattern matching algorithms to authenticate the student’s identity ( Zhao and Ye, 2010 ). This biometric is also good candidate for online exams; however, it may not always be reliable due to the complexity of recognition technology and variability in lighting, facial hair, and facial features ( Agulla et al., 2008 ; Ullah et al., 2012 ).

Audio or voice biometrics are used for speech recognition as well as authentication of the speaker. Human voice can be recognized via an automated system based on speech wave data ( Ullah et al., 2012 ). A voice biometric is highly unique, in fact it is as unique to an individual as a fingerprint ( Rudrapal et al., 2012 ). However, as with facial recognition, varying conditions such as speech speed, environmental noises, and the quality of recording technology may result in unreliable verification ( Ullah et al., 2012 ). Finally, the analysis of an individual’s typing patterns (e.g., error patterns, speed, duration of key presses) can be used to authenticate the user ( Bartlow and Cukic, 2009 ).

Checks for Text Originality

When using assessments that require a written answer, software that checks for the originality of text (such as “TurnItIn”) can help to identify work that was taken from sources without proper citation. With this method, submitted work is compared against other work held in the software’s bank to check for originality. Benefits of this method include being able to compare submitted work against work that is publicly available (as defined by the software company) to check for important degrees of overlap, as well as comparing submitted work against other assignments that have been previously submitted.

Although checking for text originality can be helpful in detecting both accidental and intentional plagiarism, there are concerns about the ethics of this practice, including copyright infringement of student work (e.g., Horovitz, 2008 ). Instructors are typically able to specify within the software whether submitted work will be stored for later comparisons (or not), and this information, along with the broader use policies, should be included specifically in the syllabus or other relevant communications with students. Additionally, when using originality-checking software, it is important to know that high overlap with other works is not necessarily indicative of plagiarized work, and there can be high rates of false positives. For example, submissions with high rates of appropriate references can return a high score for overlap simply because those references are standard across many works. Thus, instructors should refer to the full originality report so that they can use judgment as to whether high scores are actually reflective of plagiarism.

Lockdown Browsers

Lockdown browsers prevent the use of additional electronic materials during exams by blocking students from visiting external websites or using unauthorized applications on the same device as the one being used to take the assessment ( Cote et al., 2016 ). These programs take control of the entire computer system by prohibiting access to the task manager, copy and paste functions, and function keys on that device ( Percival et al., 2008 ). Though likely helpful, lockdown browsers cannot guarantee that external information will not be accessed. Students may still access information using another computer, a cell phone, class notes, etc., during an assessment. In addition to using external material, students may also cheat by making the lockdown browser program inoperative ( Percival et al., 2008 ). For these reasons, it is proposed that these programs should be used in concert with other exam security measures in order to prevent and detect cheating behaviors during exams.

Assessment-Based Methods

Given the financial and logistical concerns that may make cheating detection through online proctoring and other technological solutions unfeasible (e.g. Cluskey et al., 2011 ), and given concerns with privacy and data security, some advocate instead for changes to exam formatting (structure, presentation) that can, in turn, prevent and deter cheating at little cost ( Vachris, 1999 ; Shuey, 2002 ; , 2003 ). Below and in Table 2 , we highlight considerations for both assessment structure and assessment presentation, with particular focus on online exams, that may promote academic integrity behaviors. It is also important to note that many of these considerations are closely related, and many of these work in tandem to facilitate an honest assessment.

TABLE 2 . Preventing cheating and facilitating academic integrity through assessment structure and presentation.

The considerations provided in Table 2 have been discussed at length by other scholars. For example, Cluskey et al. (2011) have proposed online exam control procedures (OECPs), or non-proctor alternatives, to promote academic honesty. These OECPs include: offering exams at one set time, offering an exam for a brief period of time, randomizing the question sequence, presenting only one question at a time, designing the exam to occupy a limited period of time allowed for the exam, allowing access to the exam only one time, requiring the use of a lockdown browser, and changing at least one third of exam questions every term. These methods will likely not eliminate cheating entirely; however, the inclusion of these methods may decrease rates of cheating.

Ideally, online assessments are designed in such a way to reduce academic dishonesty through exam format by reducing the opportunity, incentive/pressure , and the rationalization/attitude for cheating. As discussed previously, the academic fraud triangle posits that all three of these factors lead to academic dishonesty ( Ramos, 2003 ; Becker et al., 2006 ; Varble, 2014 ). Thus, in relation to online exams, minimizing these factors may serve to encourage academic honesty. Though many of the procedures described below work well in tandem, of course, some of these procedures are incompatible with one another; for example, limiting the number of exam attempts may limit the opportunity to cheat, but allowing for multiple exam attempts may reduce the pressure to cheat. We suggest that it is important to consider a balance; cheating prevention methods will be limited in their success if students’ needs or attitudes have not also been addressed with the methods described in Individual- and Institutional-Level Methods .

This paper began by providing a review of current thought regarding the reasons why students may feel motivated to engage in behaviors that violate academic integrity. We approached this question by considering four “levels” from which to consider academic integrity: the student, the institution, the medium of delivery, and the assessment. We suggest that when examining academic integrity in the online environment, it will be necessary for continued research exploring cheating culture and the nature of, and motivation for, cheating on different types of assessments. Further, as shown, research to date has produced mixed findings in relation to whether academic dishonesty may be more or less prevalent in the online environment, and we have called for further research that examines assessment type, field of study, and student demographics (e.g., age and reason for enrolling in the course). In the latter half of this review, we detailed methods for both preventing and detecting cheating behavior, with a focus on online summative assessments. We emphasize again, though, that these methods must be considered in concert with broader consideration of the reasons and motivations that students may engage in academic dishonesty in the first place, and with explicit attention and care to student privacy and fair treatment.

Academic integrity remains an integral element of higher education. The principle values that constitute academic integrity not only uphold the reputation of a university and the value and meaning of the degrees it confers, but they also create a shared framework for professional work that is extended beyond the academy. Thus, as online studies continue to expand in post-secondary education, we believe that it will be important to have evolving scholarship and discussion regarding the maintenance of academic integrity in the online environment.

Author Contributions

All authors conceived of and outlined the project. OH completed the majority of the literature review and wrote the first draft. MN and VK added to the literature review and edited subsequent drafts.

This work was supported by an Insight Grant from the Social Sciences and Humanities Research Council of Canada to VK. MN is the current Undergraduate Chair, and VK was the past Associate Head (Teaching and Learning), of the Department of Psychology at Queen’s University.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

Queen’s University is situated on traditional Anishinaabe and Haudenosaunee Territory.

1 As a supplement to this paper, we have made two infographics available via https://osf.io/46eh7/ with a Creative Commons licence allowing reuse and distribution with attribution.

2 Given that the literature reviewed for this paper often uses the terms “academic dishonesty," “departures from academic integrity," and “cheating” interchangeably, this paper will follow this convention and not attempt to distinguish these terms.

3 In typical research conducted on academic dishonesty across online and in-person mediums, researchers define their samples as consisting of “undergraduate” and/or “graduate” students. An important consideration for future research is to specify the age ranges of the sample. It is possible that the frequency and type of cheating behavior by mature, nontraditional students and by traditional students may differ. One might hypothesize that mature students may be less motivated or have fewer opportunities to cheat than traditional students.

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Keywords: academic integrity, academic dishonesty, cheating, online courses, remote teaching

Citation: Holden OL, Norris ME and Kuhlmeier VA (2021) Academic Integrity in Online Assessment: A Research Review. Front. Educ. 6:639814. doi: 10.3389/feduc.2021.639814

Received: 09 December 2020; Accepted: 30 June 2021; Published: 14 July 2021.

Reviewed by:

Copyright © 2021 Holden, Norris and Kuhlmeier. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Valerie A. Kuhlmeier, [email protected]

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Research Article

Academic dishonesty among university students: The roles of the psychopathy, motivation, and self-efficacy

Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Institute of Psychology, University of Silesia in Katowice, Katowice, Poland

Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Affiliations Department of General Psychology, University of Padua, Padua, Italy, Institute of Psychology, Cardinal Stefan Wyszyński University in Warsaw, Warsaw, Poland

• Lidia Baran, 
• Peter K. Jonason

• Published: August 31, 2020
• https://doi.org/10.1371/journal.pone.0238141
• Reader Comments

Academic dishonesty is a common problem at universities around the world, leading to undesirable consequences for both students and the education system. To effectively address this problem, it is necessary to identify specific predispositions that promote cheating. In Polish undergraduate students ( N = 390), we examined the role of psychopathy, achievement goals, and self-efficacy as predictors of academic dishonesty. We found that the disinhibition aspect of psychopathy and mastery-goal orientation predicted the frequency of students’ academic dishonesty and mastery-goal orientation mediated the relationship between the disinhibition and meanness aspects of psychopathy and dishonesty. Furthermore, general self-efficacy moderated the indirect effect of disinhibition on academic dishonesty through mastery-goal orientation. The practical implications of the study include the identification of risk factors and potential mechanisms leading to students’ dishonest behavior that can be used to plan personalized interventions to prevent or deal with academic dishonesty.

Citation: Baran L, Jonason PK (2020) Academic dishonesty among university students: The roles of the psychopathy, motivation, and self-efficacy. PLoS ONE 15(8): e0238141. https://doi.org/10.1371/journal.pone.0238141

Editor: Angel Blanch, University of Lleida, SPAIN

Received: April 9, 2020; Accepted: August 10, 2020; Published: August 31, 2020

Copyright: © 2020 Baran, Jonason. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The database was uploaded to Open Science Framework and is available under the following address: https://osf.io/frq9v/ .

Funding: Funding was provided by the Polish National Agency for Academic Exchange ( https://nawa.gov.pl/en/ ) to P.K.J under Grant number PPN/ULM/2019/1/00019/U/00001. This funding source had no role in the study conception, design, analysis, interpretation, or decision to submit for publication.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Academic dishonesty refers to behaviors aimed at giving or receiving information from others, using unauthorized materials, and circumventing the sanctioned assessment process in an academic context [ 1 ]. The frequency of academic dishonesty reported in research indicates the global nature of this phenomenon. For example, in a study by Ternes, Babin, Woodworth, and Stephens [ 2 ] 57.3% of post-secondary students in Canada allowed another student to copy their work. Similarly, 61% of undergraduate students in Sweden copied material for coursework from a book or other publication without acknowledging the source [ 3 ]. Working together on an assignment when it should be completed as an individual was reported by 53% of students from four different Australian universities [ 4 ], and copying from someone’s paper in exams at least once was done by 36% of students from four German universities [ 5 ]. Research shows that academic dishonesty is also a major problem at Polish universities. In the study by Lupton, Chapman, and Weiss [ 6 ] 59% of the students admitted to cheating in the current class, and 83.7% to cheating at some point during college. According to a report on the plagiarism in Poland, prepared by IPPHEAE Project Consortium, 31% of students reported plagiarizing accidentally or deliberately during their studies [ 7 ].

Existing academic dishonesty prevention systems include using punishments and supervision [ 8 ], informing students about differences between honest and dishonest academic actions [ 9 ], adopting university honor codes [ 10 ], and educating students on how to write papers and conduct research correctly [ 11 ]. Although these methods lead to a reduction of academic dishonesty (see [ 12 ]), their problematic aspects include the possibility of achieving only a temporary change in behavior, limited impact on students' attitudes towards cheating, and a long implementation period [ 13 , 14 ]. Possible reasons for these difficulties include the fact that conventional prevention methods rarely address differences in students’ personality and academic motivations, which may be associated with a tendency to cheat. For example, previous studies have reported that negative emotionality was associated with positive attitudes toward plagiarism [ 15 ]; intrinsic motivation was associated with lower self-reported cheating [ 16 ]; and socially orientated human values were negatively, while personally focused values were positively correlated with academic dishonesty [ 17 ].

It is also important to remember that implementing the aforementioned methods of prevention will not lead to a reduction in academic dishonesty if faculty members do not follow and apply the established rules [ 18 ]. Faculty members often prefer not to take formal actions against dishonest students [ 19 ], and in many cases do not use the methods available to them to detect and prevent cheating [ 20 ]. However, when they do respond to academic dishonesty it is often in inconsistent ways [ 21 ]. This might suggest that, while dealing with students’ dishonesty, faculty members prefer to choose their own punitive and preventative methods, which may differ depending on the particular student and professor. If that is the case, then examining the role of individual differences in academic dishonesty could be useful not only to better understand the nature of academic transgressions but also to address faculty's informal ways of dealing with students' cheating.

The aim of the current study was to investigate relationships between personality, motivation, and academic dishonesty to understand the likelihood of cheating in academia more effectively and potentially inform faculty's personalized interventions. Of all the personality traits under investigation, psychopathy appears to be useful for this purpose, because it includes a tendency to be impulsive, to engage in sensation-seeking, and resistance to stress, all of which are associated with academic dishonesty [ 2 ]. Indeed, psychopathy is the strongest—albeit moderate in size ( r = .27)—predictor of academic dishonesty according to a recent meta-analysis of 89 effects and 50 studies [ 22 ]. In the present study, we wanted to further examine the relationship between academic dishonesty and psychopathy by using the triarchic model of psychopathy distinguishing its three phenotypic facets: boldness, meanness, and disinhibition [ 23 ] which may reveal added nuance to how this personality trait relates to academic dishonesty.

Within the triarchic conceptualization of psychopathy, boldness represents self-assurance, fearlessness, and a high tolerance for stress and unfamiliarity; meanness captures interpersonal deficits such as lack of empathy, callousness and exploitativeness; and disinhibition represents the tendency towards impulsivity, poor self-regulation and focus on immediate gratification. Because of the different neurobiological mechanisms leading to the shaping of those aspects [ 24 ], it seems likely that the tendency towards academic dishonesty may have a different etiology depending on their levels. For students with high disinhibition, cheating may result from low self-control; for those with high meanness from rebelliousness with propensity to use others; and for bold ones from emotional resiliency and sensation-seeking [ 25 – 27 ]. However, because boldness constitutes fearlessness without failed socialization [ 28 ], breaking academic rules might not be the preferred way to look for excitement among bold students. Thus, our first goal was to examine the predictive power of boldness, meanness, and disinhibition in academic dishonesty.

Furthermore, we were interested if the relationships between the psychopathy facets and academic dishonesty would be mediated by individual differences in motivations for mastery and performance. Mastery motivation is fostered by the need for achievement and associated with learning to acquire knowledge, whereas performance motivation is geared towards reducing anxiety and related to learning to prove oneself to others [ 29 ]. We expect mediation for several reasons. First, undertaking actions motivated by achievement goals is predicted by the level of positive and negative emotionality and also by activity of the behavioral activation and inhibition system [ 30 ], which also correlate with the dimensions of the triarchic model of psychopathy [ 31 ]. Second, unrestrained achievement motivation partially mediates the relationship between psychopathy and academic dishonesty, suggesting a role of achievement in understanding the relationship between psychopathy and individual differences in the propensity to cheat [ 32 ]. Third, meanness and disinhibition are negatively and boldness positively correlated with conscientiousness and its facets [ 33 , 34 ]. This fact may play an important role in students’ willingness to exert and control themselves to achieve academic goals and the particular way to do it [ 35 ]. Moreover, research on mastery-goal orientation suggests it is correlated negatively with academic dishonesty and views of the acceptability of academic dishonesty [ 36 – 38 ] and that the change from mastery to performance-based learning environment lead to increased levels of dishonesty [ 39 ].

Therefore, we hypothesized that students with a high level of disinhibition may have difficulties studying because of their need for immediate gratification and lack of impulse control, and in turn, cheat to pass classes. Bold students could want to acquire vast knowledge and high competences because of their high self-assurance, social dominance, and a high tolerance for stress without resorting to fraud. Lastly, students with a high level of meanness may be less prone towards mastery through hard work and learning because of their susceptibility to boredom, tendency to break the rules, and to exploit others to their advantage, perhaps by copying or using other students’ work. Because performance-goal orientation can be driven by the fear of performing worse than others, no specific hypothesis was generated regarding its relation to psychopathy (characterized by a lack of fear).

Besides behavioral tendencies based on personality traits and specific motives to learn, another closely related predictor of academic dishonesty is general self-efficacy. People with high levels of general self-efficacy exercise control over challenging demands and their behavior [ 40 ] and perform better in academic context because of their heightened ability to solve problems and process information [ 41 ]. On the other hand, low levels of general self-efficacy in the academic context can lead to reduced effort and attention focused on the task, which may result in a higher probability of frauds to achieve or maintain a certain level of academic performance [ 42 , 43 ]. Because competence expectancies are important antecedents of holding an achievement goal orientation [ 44 , 45 ] it seems possible that general self-efficacy might moderate the relation between psychopathy facets and academic dishonesty mediated by achievement goals. Thus, we hypothesize that high general self-efficacy will reduce the indirect effects for disinhibition and meanness (i.e., negative moderation effect) and amplify it for boldness (i.e., positive moderation effect).

In sum, we examine the relationships between three facets of psychopathy and academic dishonesty, the possible role of achievement goals as a mediators for those relations, and lastly the possible role of general self-efficacy as a moderator of those mediation models. By analyzing the facets of psychopathy independently, we can determine their unique relationship with the tendency to cheat and thus more accurately predict the risk of dishonest behavior for students with a high level of each of the facet. In addition, investigating indirect effects and interactions between personality and motivation may describe the psychological processes that may lead to cheating and can potentially be used in planning preventive actions.

Materials and methods

Participants and procedure.

The participants were 390 Polish university students and residents (100% White, 74% female) with an average age of 23 ( SD = 3.39, Range = 19–56) years. Participants self-identified as students in social sciences (17%), humanities (12%), science and technology (24%), law and administration (22%), and medical sciences (23%); 7 failed to respond (2%). In addition, participants were first-year (19%), second-year (16%), third-year (31%), fourth-year (13%), fifth-year (13%), and doctoral students (2%); 23 failed to respond (6%).

We established the required sample size as 290 participants, following Tabachnick and Fidell [ 46 ] guidelines and gave ourselves three months to collect it to avoid concerns with power and p- hacking, respectively. The study was approved by Ethics Committee of the Faculty of Pedagogy and Psychology (University of Silesia in Katowice) and was conducted online through the Webankieta platform to maximize the anonymity and security of the participants. An invitation to participate in the project was sent to 28 largest Polish universities by enrollment, with a request to publish it on the universities' websites. The link to the survey directed the participants to a detailed description of the research and the rules of participation. After consenting to participate, students completed online questionnaires and, at the end, they were asked if they wanted to receive a summary of the general results and take part in a prize drawing (after the end of the study, five randomly chosen participants received vouchers for online personal development courses). The present study was part of a larger investigation that aimed to examine psychological determinants and predictors of academic dishonesty.

Psychopathy was measured with the TriPM-41 [ 34 ], the shortened Polish adaptation of the Triarchic Psychopathy Measure [ 47 ]. Participants rated statements on a 4-point scale (0 = completely false ; 1 = somewhat false ; 2 = somewhat true ; 3 = completely true ). Items were summed to create indexes for three subscales: disinhibition (16 items, e.g., “I jump into things without thinking”; Cronbach’s α = .83), meanness (10 items, e.g., “I don't have much sympathy for people”; α = .92), and boldness (15 items, e.g., “I'm a born leader”; α = .88).

Achievement goals were measured with the Polish translation of the Achievement Goals Questionnaire-Revised [ 29 ]. Participants reported their agreement (1 = strongly disagree ; 5 = strongly agree ) with statements such as “My aim is to completely master the material presented in this class” (i.e., mastery-goal orientation, 6 items) or “My aim is to perform well relative to other students” (i.e., performance-goal orientation, 6 items). Items were summed to calculate mastery (α = .80) and performance (α = .87) goal orientation indexes.

The Polish translation of the New General Self-Efficacy Scale [ 48 ] was used to measure general self-efficacy (e.g., “Even when things are tough, I can perform quite well”). Participants were asked how much they agreed (1 = strongly disagree ; 5 = strongly agree ) with eight items, which were summed to create the general self-efficacy index (α = .89).

Academic dishonesty was estimated with the Academic Dishonesty Scale [ 49 ], which is a list of 16 academically dishonest behaviors (e.g., “Using crib notes during test or exam” or “Falsifying bibliography”). Participants rate the frequency (0 = never ; 4 = many times ) of committing each behavior during their years of studies. Items were summed to create the academic dishonesty index (α = .83).

Data analysis

Descriptive statistics were calculated with JASP (v0.9.0.0), correlations with STATISTICA (v13.1), and regression, mediation, and moderated mediation with SPSS (v25). In the mediation analysis we used model 4 in macro PROCESS 2.16.3 (10,000 bootstrapped samples) and for the moderated mediations model 7 in macro PROCESS 2.16.3 (10,000 bootstrapped samples). Analyzes were carried out on the responses from 390 fully completed surveys. Because of mixed results in previous studies concerning psychopathy and academic dishonesty levels in men and women (see [ 50 , 51 ]) we conducted analyses on the overall results and also separately in each sex. The database was uploaded to Open Science Framework and is available under the following address: https://osf.io/frq9v/

Descriptive statistics, sex differences tests (see Bottom Panel), and correlations (see Top Panel) for all measured variables are presented in Table 1 . Academic dishonesty was positively correlated with meanness and disinhibition, and negatively correlated with mastery-goal orientation and general self-efficacy. Mastery-goal orientation was positively correlated with boldness and general self-efficacy, and negatively correlated with meanness and disinhibition. Performance-goal orientation was positively correlated with meanness. General self-efficacy was positively correlated with boldness and negatively correlated with meanness and disinhibition. We found only three cases where these correlations were moderated by participant’s sex. The correlation between performance and mastery-goal orientation was stronger ( z = -1.85, p = .03) in men ( r = .51, p < .01) than in women ( r = .34, p < .01). The correlation between mastery-goal orientation and meanness was stronger ( z = 2.00, p = .02) in men ( r = -.28, p < .01) than in women ( r = -.05, ns ). And the correlation between disinhibition and academic dishonesty was stronger ( z = 1.72, p = .04) in women ( r = .39, p < .01) than in men ( r = .20, p < .01). If we adjust for error inflation for multiple comparisons ( p < .007) for these moderation tests, none of the Fisher’s z tests were significant. Therefore, we conclude the correlations were generally similar in the sexes. Men scored higher than women on meanness and disinhibition.

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https://doi.org/10.1371/journal.pone.0238141.t001

To test the contribution of personality and motivation variables in predicting academic dishonesty, we conducted a standard multiple regression where the model explained 23% of the variance in academic dishonesty [ F (6, 383) = 18.60, p < .001]. The residuals for boldness ( β = .12, p = .04), disinhibition ( β = .27, p < .01), and a mastery-goal orientation ( β = -.39, p < .01) were correlated with academic dishonesty. Additional regression analysis revealed that both mastery-goal orientation and disinhibition strengthened the association between boldness and academic dishonesty, which on its own was not a predictor of the frequency of cheating–suppressor effect (results of hierarchical regression showed that after adding boldness to the model explained variance increased by 1% [Δ F (1, 383) = 4.40, p = .04]).

To examine whether achievement goals mediated the associations between psychopathy and academic dishonesty we conducted a series of mediation analyses.

As shown in Table 2 (see Left Panel), mastery-goal orientation mediated the relation between facets of psychopathy and academic dishonesty (i.e., none of the indirect effects CIs contained zero), and performance-goal orientation was not a mediator of those relations (see Right Panel; all of the indirect effects CIs contained zero). Mastery-goal orientation mediated relation between disinhibition and academic dishonesty (i.e., initial 𝛽 Step 1 = .32, p < .001; 𝛽 Step 2 = .24, p < .001), and the relationship between meanness and academic dishonesty (i.e., 𝛽 Step 1 = .10, p < .05; 𝛽 Step 2 = .05, p = .29). Initial non-significant negative relation between boldness and academic dishonesty (𝛽 = -.0001, p = .99) stayed unrelated after adding mastery-goal orientation to the model, but the value for the relation coefficient was higher and positive (𝛽 = .07, p = .12) suggesting a nonsignificant suppression effect.

https://doi.org/10.1371/journal.pone.0238141.t002

To test if the level of general self-efficacy moderated the aforementioned relationships between psychopathy, achievement goals, and academic dishonesty we ran a series of moderated mediations. Index for moderated mediation was significant only for the model with disinhibition and mastery-goal orientation ( - 0.03; 95% CI: -0.70, -0.003), however, the same analyses ran separately for men ( - 0.03; 95% CI: -0.13, 0.05) and women ( - 0.04; 95% CI: -0.08, -0.01) revealed moderated mediation only in women (therefore, we do not report these analyses in men; they can be obtained from the first author). Estimates for that model are presented in Table 3 .

https://doi.org/10.1371/journal.pone.0238141.t003

Women with high levels of disinhibition manifesting low level of mastery-goal orientation (see Left Panel, line A1) declared higher levels of academic dishonesty (see Right Panel, line B). An interaction between disinhibition and general self-efficacy (see Left Panel, line A3) with the significant, negative index for moderated mediation means that the indirect effect of disinhibition on academic dishonesty through mastery-goal orientation is negatively moderated by general self-efficacy. The higher the level of the moderator, the weaker the effect of mediation, and for moderator values above one standard deviation from mean mediation become non-significant (95% CI: -0.01, 0.09). In sum, the mastery-goal orientation partially mediated the associations that disinhibition had with academic dishonesty, however, this effect was absent for people with high levels of general self-efficacy.

Discussion and limitations

Psychopathy is an important predictor of engaging in unethical behaviors [ 52 ], including in an academic context [ 53 ]. In the present study, we examined the relationships between facets of psychopathy, as described in the triarchic model of psychopathy (i.e. disinhibition, meanness, and boldness), and the frequency of academic dishonesty among students. We revealed that students with higher levels of meanness and disinhibition, but not boldness, reported more frequent academic dishonesty during their tertiary study.

In the case of meanness, this relationship may indicate a tendency for dishonesty resulting from a lack of fear and, consequently, a diminished impact of the perceived risk of being caught cheating, sensation-seeking that involves engaging in destructive behavior regardless of possible negative consequences of such actions, and a propensity to exploit other student’s work or knowledge to pass classes [ 23 , 54 ]. The association between disinhibition and academic dishonesty may indicate impulsive cheating resulting from self-control problems (see [ 55 ]), and an inability to predict possible negative consequences of cheating [ 26 ]. The fact that academic dishonesty and boldness were uncorrelated may indicate that even though bold students can perform successfully in stressful situations and have high levels of sensation-seeking, those features are unrelated to the tendency to cheat in the academic context. It confirms that the “successful psychopath” [ 56 ] may be characterized by boldness but not antisocial behavior. Of all the facets of psychopathy, disinhibition was the strongest predictor of academic dishonesty, which confirms the role of impulsivity in predicting risky behavior [ 57 , 58 ], and the role of delaying gratification in refraining from academic transgressions [ 59 ].

Beyond these basic associations, we also examined the role of achievement goals as mediators for the relationships between psychopathy facets and academic dishonesty. Mastery-goal orientation mediated the relationships between two psychopathy facets and academic dishonesty. Both meanness and disinhibition led to low levels of students’ mastery-goal orientation which, in turn, contributed to cheating in the academic context. Low mastery-goal orientation might result from the fact that those who are characterized by meanness may have a propensity to be rebellious (e.g., disregard for formal responsibilities, low diligence, and sensitivity to rewards) and those who are characterized by disinhibition may have a propensity for impulsivity (e.g., inability to postpone gratification or control impulses, high behavioral activation system). Without motivation to acquire knowledge, students may cheat to achieve academic goals with no regard to the fairness (i.e., high meanness) or the consequences (i.e., high disinhibition) of their actions [ 31 – 33 ]. In the case of boldness, the result of the mediation analysis might indicate a cooperative or reciprocal suppression effect, however, it should not be trusted because the main effect path did not pass the null hypothesis threshold when the potential suppressor was included in the model. Nonetheless, it seems possible that a particular configuration of boldness and disinhibition could lead to the interactive effect of those facets on the other variables [ 26 ]. Performance-goal orientation did not mediate the relationships between psychopathy facets and academic dishonesty, probably because bold, mean, and disinhibited students are not motivated by the fear to perform worse than others [ 60 ].

Lastly, we tested if general self-efficacy acts as a moderator of these mediation models and found evidence that it moderated the indirect effect of disinhibition on academic dishonesty through mastery-goal orientation. This means that disinhibited students who have a high sense of perceived ability to control their chances for success or failure, might be able to overcome the tendency to cheat resulting from their personality (i.e., high impulsiveness), and motivational (i.e., low motivation to learn) predispositions. However, that effect was found only for women, limiting any insights that can be drawn about men. Previous research showed that an increase in general self-efficacy reduced the risk of suicide among women [ 61 ]. Moreover, Portnoy, Legee, Raine, Choy, and Rudo-Hutt [ 62 ] found that low resting heart rate was associated with more frequent academic dishonesty in female students, and that self-control and sensation-seeking mediated this relationship. Thus, along with the observed lower level of disinhibition for female students, it appears that self-regulation abilities may play a different role for men and women’s performance, and also that deficits in self-control might not lead to the same behavioral tendencies in the sexes (see [ 63 ]). However, because of the cross-sectional nature of our study and an uneven number of men and women in the sample, this needs to be investigated further.

In the present study, we aimed to combine personality and motivation variables to describe the possible process leading to academic dishonesty assessed with a behavioral measure. Because Polish students do not constitute a typical W.E.I.R.D. sample (i.e., Western, Educated, Industrialized, Rich, and Democratic), presented results can be used to generalize conclusions from research on academic dishonesty beyond typical W.E.I.R.D cultures. However, our study is not without limitations. First, the measurement of academic dishonesty was based on self-report, which, even after maximizing anonymity of the measurement, might have attenuated our results concerning the frequency of cheating. Thus, future studies should focus on measuring actual dishonest academic behavior. Second, we examined academic dishonesty as an overall frequency of committing different acts of cheating, which reflects the general propensity to cheat. It could be useful to further investigate the predictive power of described models in experiments, focused on the specific type of dishonest behavior. Third, the obtained range of academic dishonesty scores might result from sampling bias, which would require using different sampling procedure in future studies, or from non-normal distribution of academic dishonesty, which would be consistent with the results of the previous studies [ 2 – 4 ]. Fourth, we tested mediation models in a cross-sectional study with a one-time point measurement, which require cautious interpretation. Future studies could use longitudinal methods; starting at the beginning of the first year and continuing over the course of their studies to capture the influence of personality, achievement goals, and general self-efficacy on the academic dishonesty of students in a more robust manner. Despite these shortcomings, our study is the first attempt (we know of) to integrate the triarchic model of psychopathy, general self-efficacy, and achievement goals to predict academic dishonesty, showing potential for further investigation in this area.

Implications and conclusions

Preventing academic dishonesty is often made difficult by the lack of centralized and formalized university policies concerning cheating, faculty reluctance to take formal action against dishonest students, and limited attention paid to students’ personal characteristics associated with a tendency to cheat [ 64 ]. Based on the results of our study, lecturers might overcome those difficulties by: maximizing the amount of oral examinations to deal with the risk of cheating by disinhibited and mean students; enhancing students’ mastery-goal orientation, for example, by increasing use of competency-based assessment; enhancing students’ self-efficacy in academic context, for example, by providing spaced assessed tasks, and the opportunity to practice skills needed for their fulfillment. In the case of dealing with actual dishonest behavior, the fact that teachers prefer to warn students rather than fail them [ 19 ] might suggest indifference to academic integrity rules, reluctance to initiate time-consuming formal procedures against cheating, or teachers’ preference toward autonomy to deal with dishonesty. Therefore, a useful solution could be to assess which areas need to be improved for a particular student (e.g., knowledge about plagiarism, ability to delay gratification, or treating acquisition of knowledge as a value) and to allow the teacher to choose an effective way to remedy them.

In sum, we presented evidence that disinhibition and meanness are associated with the frequency of committing academic dishonesty. We described the possible underlying mechanism of those relations involving mediation effects of the mastery-goal orientation and, in the case of disinhibition, also a moderation effect of the general self-efficacy. Our research can be used by teachers to better identify factors conducive to dishonesty and to modulate their responses to fraud based on the personality and motivational predispositions of students.

Supporting information

S1 table. descriptive statistics and correlations for academically dishonest behaviors..

https://doi.org/10.1371/journal.pone.0238141.s001

Acknowledgments

We would like to thank Dr Guy Curtis for his comments and suggestions on the article.

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• Published: 05 May 2022

Reconceptualizing academic dishonesty as a struggle for intersubjective recognition: a new theoretical model

• Jasper Roe 1  

Humanities and Social Sciences Communications volume  9 , Article number:  157 ( 2022 ) Cite this article

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Renewed interest in academic dishonesty (AD) has occurred as a result of the changes to society and higher education during the COVID-19 pandemic. Despite a broad body of research investigating why and how students engage in intentional violations of principles of academic integrity, the causes of these behaviors remain uncertain. In order to fully address the overarching issue of why students engage in academically dishonest practices, social philosophy can be invoked. This article reviews the current research on AD in higher education, and then seeks to develop a new theoretical understanding based on Axel Honneth’s (1995) Theory of Recognition, positing that it is not a moral deficit that drives students to commit such acts, but a struggle for intersubjective recognition and a subtle form of privatized resistance. This offers a universal model for interpreting and understanding the position of the student in higher education, while offering insight into a social pathology, namely, the social pressure that requires higher education to be viewed as an instrumental rather than intrinsic value.

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Introduction

Violations of academic norms and standards can be a cause of “moral panic” among those working in academia (Venera-Mihaela and Mares, 2021 ), and such acts have the potential to cause great societal damage. Students who reported committing such acts also expressed likelihood to be dishonest in other areas of life (Guerrero-Dib et al., 2020 ). Lynch et al. ( 2021 ) found that in nursing education, dishonest behaviors may continue into clinical practice, potentially causing grave consequences. There is also a widespread understanding outside of academia that such behaviors are socially intolerable. As an example, in 2021 three German ministers were pushed to leave their office as the result of plagiarism in their respective Ph.D theses (Oltermann, 2021 ).

This article describes the current state of the research base in relation to these concepts, before seeking to reinterpret the root causes of transgressions against the norms of academia through application of Honneth’s ( 1995 ) Theory of Recognition. Prior to doing so, the difference between academic integrity (AI) and academic dishonesty (AD) needs to be clarified. As the “moral code” of academia, AI is built on the dedication to values of honesty, fairness, trust, respect, and responsibility (Lynch et al., 2021 ). AD on the other hand, refers to behaviors which seek to violate the code of AI. The International Center for Academic Integrity ( 2022 ) includes plagiarism, cheating, lying, and deception under the umbrella of AD. A distinction must be made when considering cases in which students have unintentionally violated principles of AI, for example using patchwriting or misquotation. As AI is reliant on an understanding of explicit and implicit norms (Venera-Mihaela and Mares, 2021 ) many students may inadvertently violate such norms while engaged in the learning process. In this article I seek to focus not on such cases, but on intentional cases of AD, in which the student aims to deliberately violate rules in order to gain an advantage by deception, defrauding, or misleading the assessor(s).

Many of those working in higher education will have encountered AD. DiPaulo ( 2022 ) found that 80% of preservice teachers surveyed undertook AD behaviors while engaged in their course of study, including sharing information among peer groups, and 68% engaged in more formalized “cheating”. Large-scale follow up studies over ten years have found that over 60% of students have cheated in some form in their academic study (International Center for Academic Integrity, 2022 ). Some authors have reported figures as high as 95% ( N  = 1127) of students engaging in forms of cheating (Ives et al., 2017 ). In the USA, the figure of 68% was reported for students who had cheated in the past, rising to 75% when asked if they would cheat in the future (Chapman et al., 2004 ). In short, AD is firmly embedded in higher education, and although studies on AI and AD have been published since the 1940s, for example Drake ( 1941 ), the disruptive effects of the COVID-19 pandemic have resulted in an increased focus on this topic. It has been reported that AI violations have increased directly due to online learning instituted as a result of the COVID-19 pandemic, and empirical research has demonstrated that students may also believe this to be the case, with 81% of STEM students surveyed ( N  = 299) believing that online learning caused an increase in cheating (Walsh et al., 2021 ). In the mainstream media, it has been claimed that occurrences of cheating are “soaring” during the online era of the pandemic (Dey, 2021 ).

How educators and institutions are dealing with these increases in AI violations and AD varies. High-technology methods include the expanded use of new software for online proctoring and “lockdown” browsers to limit students’ access to external sources on a personal computer, and over 20 different forms of artificial intelligence and machine learning technologies are now available to detect cheating, including those using advanced techniques to maintain integrity such as biometric systems of identification, multi-factor authentication, and blockchain applications (Slusky, 2020 ). Walsh et al. ( 2021 ) point out that this is not the only solution to perceived increases in AI and AD violations, as low-technology methods, such as altering summative assessments and using open-book examinations are also in use. These tools tend to focus on attacking the symptoms of AD, rather than the cause. Consequently, focus needs to be redirected away from locating and combating AD, and towards understanding the social and moral reasoning that underpins these behaviors. Following this, I will analyze the current understanding of AD, and then seek to reinterpret AD through the moral philosophy and social Theory of Recognition developed by Axel Honneth ( 1995 ).

Why students commit AD: current theories and understanding

In the context of COVID-19, Walsh et al. ( 2021 ) found that students surveyed attributed a perceived increase in AD behaviors to four social and psychological theories: Game Theory, Kohlberg’s Theory of Moral Development, Neutralization Theory, and the Theory of Planned Behavior (TPB) (Ajzen, 1991 ). Each of these theories posits a philosophy that explains why the student commits AD, for example through playing a “game” of cat and mouse between student and teacher (Game Theory), developing a stronger set of moral beliefs over time (Kohlberg’s Theory of Moral Development), rationalization of the violations (Neutralization Theory), and the combination of the intention to commit AD combined with a perceived opportunity (TPB) (Walsh et al., 2021 )

The theories above may then explain some factors or motivations for engaging in AD, but do not tell the entire story. Taking TPB as an example, this theory may explain why cheating occurs when it occurs (for example, when the opportunity arises coupled with the intention), but does not explain how the positive attitude towards AD was formed initially. To this end, research has aimed to identify relationships between certain factors and the likelihood of committing AD. These can be described as belonging to four overarching categories: attitudes, traits, language and culture, and student experience.

Category 1: attitudes towards AD among students, peers, and instructors

One of the most clearly established factors which predict AD is the student’s attitude towards cheating in general (which under TPB, may form part of the “intention” to cheat). A large body of research supports this point (Eriksson and McGee, 2015 ; Ives et al., 2017 ; Hendy and Montargot, 2019 ; Peled et al., 2019 ; Zhang et al., 2021 ; DiPaulo, 2022 ). Whitley et al. ( 1999 ) equally found through a review of 107 studies that viewing cheating positively was a causative factor for AD, along with expectations of the outcome of cheating, prior history of cheating, and perceived rewards. More recently Zhang et al. ( 2018 ) studied 2009 students across eastern China, finding that those who viewed AD as less serious or unimportant were more likely to engage in it.

Teachers’ and peers’ personal attitudes towards AD have also been shown to have an effect on the likelihood of engaging in acts of AD (McCabe et al., 2001 , 2012 ; Maloshonok and Shmeleva, 2019 ). McCabe et al. ( 2001 ) found that when AD behaviors are tolerated by instructors, cheating can increase, and Anderman et al. ( 2009 ) and Yu et al. ( 2018 ) equally found that the students’ opinion of the teacher, and the teacher’s view of AD inversely correlated with its occurrence, while Robinson-Zanartu et al. ( 2005 ) surveyed 270 faculty members, finding that how severely the faculty viewed the violation strongly influenced the severity of the consequences they would seek to impose on the violator.

The effect of peer influence is also clearly established as a causative factor, and as a result AD is more likely to increase when students perceive that others are acting similarly (McCabe, 2016 ). This finding is also important in demonstrating a principle of social solidarity among peers. For example, in a survey assessing student behavior at a small liberal arts university, Papp and Wertz ( 2009 ) found that over 75% of students would not report a witnessed occurrence of cheating, and over 80% would not report a friend.

Category 2: personality traits, gender, and age

Other factors have equally been attributed to the likelihood of engaging in AD. Students who are highly achieving may be less likely to commit acts of AD (McCabe and Trevino, 1997 ; McCabe and Pavela, 2004 ; McCabe et al., 2012 ). How students view themselves (Ng, 2020 ) plays a role, as does students’ self-efficacy (Marsden et al., 2005 ). Students who are excitement-seeking may engage in AD more often (de Bruin and Rudnick, 2007 ) and students who demonstrate personality traits of deviance and low self-restraint may similarly be more inclined to cheat (Jensen and Jetten, 2018 ).

Some studies have focused on gender and age (McCabe and Trevino, 1997 ). Males have been more commonly identified as likely to engage in AD. Szabo and Underwood ( 2004 ) found that 68% of males cheated in assessments compared to just 39% of females, and those in their third year of study were less inclined to cheat in assessment than those in their second or initial year, while (Yang, 2012 ) found that female graduate students were more likely to hold critical views of AD than males, and that doctoral students were less likely to commit AD than master’s students. However, this is by no means certain, as other studies, for example Ives et al. ( 2017 ) found no association between academic achievement, field of study, or year of study.

Finally, the relationship between the learning space and the student may play a role in causes of AD, particularly as technology disrupts the traditional classroom experience (Venera-Mihaela and Mares, 2021 ). In terms of the impact of COVID-19, although the move to online learning has resulted in perceived increases in AD among students (Walsh et al., 2021 ). In sum, there is little clear evidence of a definite pattern concerning these variables.

Category 3: international students: language, culture, or none of the above?

Another area that has commanded attention in the literature is that of international students and students who speak English as a foreign or second language, along with the cultural background of these groups, presented here as two closely related subjects. Language proficiency has been implicated in AD and correlations have been identified between ability, training, and occurrences of AD (Bretag, 2007 ; Perkins et al., 2018 ; 2020 ), although further research is needed in this area. Bertram Gallant et al. ( 2014 ) posit that in regards to AD, the international student population may display greater vulnerability due to a lack of knowledge on behavioral standards in Western universities, or may not have the same fear of consequences, whereas Hendy et al. ( 2021 ) found that the wide variance between AD behaviors among French students, U.S. students and Greek students could be explained by cultural differences, and McCabe et al. ( 2008 ) found that Lebanese university students are influenced by collectivist societal norms in comparison the individual-centric society in the USA. International students or students from non-North American cultural backgrounds may also demonstrate a higher rate of AD (Park, 2003 ). Among doctoral students Cutri et al. ( 2021 ) identify that both feelings of inadequacy (“imposter syndrome”) and cultural differences explain the causes of AD. As with other affecting factors, the research is conflicting. Marshall et al. ( 2022 ) for example found that among Vietnamese students studying abroad and local PG students in New Zealand, both groups held significant understanding of plagiarism and held negative attitudes towards plagiarism, suggesting that culture is not an acceptable explanation for plagiarism behaviors and results in a simplistic approach and potential bias. Equally scholars such as Phan ( 2004 ) have posited that such cultural notions for how students behave in university are often based on inaccurate stereotypes. In such a case “culture” as a category can be seen as misrecognition itself, categorizing the individual and explaining complex behaviors in a simplistic manner.

Category 4: stress and the student experience

A wide range of research describing the causes and variables predicting student engagement in AD is available, but with no single thread of agreement and little large-scale replication of results. There is however, a more universal factor posited for engagement in AD, and it is in this factor that I ground the use of the Theory of Recognition. This is the pressures, stresses, and struggles of participating in higher education, and the societal pressure to complete education as quickly as possible, with as high a mark as possible. It is well established that participating in higher education can be a challenging experience. Tindall et al. ( 2021 ) point out that HE students demonstrate above-average levels of mental illness and nervous disorders as evidence of this. The authors identify that this may link to the likelihood of cheating or other academically dishonest acts, as “negative emotionality” in this sense may drive AD (Tindall et al., 2021 ). Other research has similarly found a link between mental health and likelihood to engage in AD, with a focus on the pressurized, high-stress student experience as a causative factor (Devlin and Gray, 2007 ). In the media this is also commonly recognized. Lodhia ( 2018 ) writes in The Guardian that today’s HE students must focus on obtaining a qualification and their subsequent recognition in the labor market, rather than focusing on their education. This is fundamentally the driving force in which recognition theory can be applied in understanding the motivation for AD. This interpretation could also help to explain why there is a perceived increase in AD among international students, as it has also been argued that cases among this group could stem from dealing with a broader range of issues resulting from cultural adjustment, living abroad, and other social and financial issues, which lead to “out of character” decision making as a result of emotional distress (Lynch et al., 2021 ).

In summarizing the research based so far, there are no firm answers as to why students commit AD, although some factors may point to circumstances in which the opportunity for AD is more likely to be taken. I argue in the following section that all of the factors discussed, and the theories posited, may be contributory—but that the overarching cause of AD behaviors is driven by the stressors placed on students to view education as instrumental in achieving recognition, and that this emphasis on completion of HE study at the fastest rate possible, with the highest grades possible, is itself a social pathology, as it fits Honneth’s ( 2014 ) definition, as a social development, which “significantly impairs the ability to take part rationally in important forms of social cooperation”—in this case, formal education.

Recognition theory and education

The Theory of Recognition described in Honneth’s ( 1995 ) work focuses on the role of recognition and disrespect as aspects of common moral experience between individuals. Honneth ( 1995 ) postulates that to attain freedom, humans must develop stable self-relation by achieving intersubjective recognition. If the individual is unable to achieve such recognition despite it being deserved, the result is that of suffering disrespect, which can impact upon and even lead to the destruction of the self. Further to this, recognition is not given freely; subjects must participate in a struggle, which can take various forms, ranging from verbal discussion all the way to entering into violent conflict, or fighting in a war (Huttunen and Murphy, 2012 ). In the context of academia, students may struggle for recognition through the achievement of grades, through discussion, criticism, or even through subtle movements and motions; a nod of agreement or look of disdain can similarly function as an action, which defines an attempt to gain intersubjective recognition or protest against disrespect.

To date, despite its importance and application in sociology and philosophy, recognition theory has received limited attention in education. Sandberg ( 2016 ) envisioned adult learners re-entering the workforce through adult education as engaged in a struggle for recognition, stuck between the isolation and disconnection from society that results from being excluded from the workforce, and the struggle of engaging in further learning to rejoin and experience stability and participation in the labor market. Sandberg and Kubiak ( 2013 ) also argue that in higher education, Honneth’s theory can be used to develop transformative learning, through the identification that teachers must develop respect for themselves primarily, as this is a condition of the ability to recognize rights in others, in order to imbue self-confidence in students. The results of this, it is argued, would produce democracy in classrooms and then in society as a result (Sandberg and Kubiak, 2013 ).

There are three areas of self-relation that must be developed under this theory in order to achieve positive self-relation, including self-confidence, self-respect, and self-esteem, and these are developed at three different sites of struggle for recognition (Huttunen and Murphy, 2012 ). Self-confidence develops through the individual’s primary relationships. Self-respect is found in the acknowledgement of a person as having legal rights. Self-esteem on the other hand, is gained from understanding that work or actions are acknowledged, and this is the highest form of recognition (Huttunen and Murphy, 2012 ). In relation to higher education, the recognition of passing an assessment, gaining a degree, or succeeding in an element of study can provide this form of intersubjective recognition in the domain of self-esteem, in the form of approval from peers, instructors, or parents, mentors and guardians. An acknowledgement from a teacher that an examination is unfair, or the remediation of an unjust punishment can equally function in this manner.

The struggle for recognition can also be a struggle against ideologies that are present in education and society at large. Honneth ( 2014 ) states that education is a process of internalizing norms, including of the performance-orientation required in the labor market. One example of an ideology that supports this is the merit principle (Herzog, 2016 ), which leads to the subordination of enjoyment of learning to the obtaining of as high a “score” as possible in assessment and outcome. Lodhia ( 2018 ) equally identifies this ideology as leading to greater numbers of students engaging in AD, as results must be prioritized over the engagement and enjoyment of learning. Students who feel that this ideology is unjust, whether consciously or unconsciously, may then feel jaded at the imposition of this ideology by society-at-large and may suffer from disrespect (Herzog, 2016 ), or suffer from great psychological stress, which forces the actions of AD. In this sense, AD can be viewed as part of a social process described by Honneth ( 2014 ), in which a social group (students) develop moral doubts about an aspect or element of the social order—in this case comprising assessment, the institution, the program, the teacher, or instrumental ideology of education, i.e., the merit principle ideology. The committing of transgressions against established norms of academic integrity can then be viewed as a struggle against this - with the result of such behaviors being “for the right reasons” but in a way that unintentionally causes the potential for social harm.

Understanding academic dishonesty as a struggle for recognition

Recognition theory can help us to understand why it is that students know that AD carries severe consequences yet continue to engage in it. In relating this to a struggle for recognition, the argument is summarized by Daniel et al. ( 1994 ) who state that the role of education is to help students to self-actualize, and if that goal is impeded, then cheating or engaging in AD is the only way in which they can continue. This is especially relevant to the tendency to “privatize discontent” (Honneth, 2014 , p. 248) in modern society, and explains why such actions are completed privately rather than in mass organized groups or through raising verbal discussions; this may also explain why when students perceive that their peers are also engaging in AD, they will be more inclined to join in (McCabe, 2016 ). Petrovskaya et al. ( 2011 ) highlight through an internal critique of nursing education that the ideals of academic life include thinking in a free, creative, and critical manner, yet these values do not correlate to some institutionalized practices of academia, which are influenced by the research industry and instrumental reason. This suggests that it is the reification of academia, which results in struggles for recognition including, under this approach, acts of academic dishonesty.

Students who engage in higher education seek the “good life” (Sandberg and Kubiak, 2013 ) by hoping to gain future employment, achieve personal growth, or to please others. The “good life” then, may entail personal or financial success, or in the case of students who are encouraged or pressured into higher education as a result of societal or familial norms it may entail freedom from such pressure on completion. To gain the recognition of completion of higher education requires success in formal assessment. Students are required to view their study as instrumental in obtaining the “good life” and passing their assessments quickly and effectively becomes part of this struggle for recognition. Those who do well may receive praise from peer group members, family members, or teaching faculty in the case of unblinded assessment, thus leading to higher recognition in the dimension of esteem. On the other hand, the consequences of failure to achieve the required standard may lead to the opposite.

This may be why students understand the risk yet still commit AD. In order to achieve their goals, to attain “the good life” (Sandberg and Kubiak, 2013 ) and to get past the impediments to their self-actualization (Daniel et al., 1994 ) they must achieve recognition of their successful participation in assessment by any means, with as high a grade as possible, and as quickly as possible, following the pattern Honneth ( 2014 ) defines as internalization of norms of performance orientation. If students have doubts in their ability and lack the positive self-relation to confidently attempt an assessment by themselves, are critical of the ideology of the assessment, or are convinced of their inability to pass, then they are faced with two choices: risk the suffering of disrespect if caught engaging in AD or risk the suffering of disrespect by failing the assessment. It is also possible to view more banal motivations such as finding the subject uninteresting or not related to the student’s self-identity (Venera-Mihaela and Mares, 2021 ) as part of a struggle for recognition. To this end, it is required to reframe the students’ motivations and path to the future good life. If the subject is uninteresting or not related to what the student defines as essential for themselves, then AD is a subtle act of resistance.

To further explain why students may choose to risk the suffering of disrespect from being caught engaging in AD, versus failing an assessment, neutralization theory may play a role. In this sense, it is less threatening to self-relation to engage in rationalization of the cheating behavior, by for example blaming another party (the instructor makes the assessments too difficult) or referring to a different system of values (the assessment is not important, I do not care about it) (Walsh et al., 2021 ). This is comparatively less consequential for self-relation compared to the risk of gaining a failing grade despite trying to succeed. One additional aspect of this understanding of AD is that it results in a circular social pathology, as the act of committing AD itself entails disrespect. By deceiving the individual responsible for marking an assessment with a false promise of authenticity, the marker is disrespected. By disadvantaging others who do engage authentically in assessment, the other assessment-takers are disrespected. Committing an act of AD then is a final attempt to save the self by disrespecting others. To demonstrate the relation between AD and recognition further, the dimensions of the theory can be mapped to components of AD. Table 1 demonstrates the domains of the Theory of Disrespect, while Table 2 develops this to account for acts of AD.

In relation to Table 1 , the applicable mode of recognition regarding AD is social esteem. In this case, in higher education, we require recognition of our traits and abilities through assessment in order to develop positive self-relation, which is magnified by our overall completion of a program (such as a degree), while enabling us to achieve a version of “the good life” (for example, a stable career). If we are unable to do so, or we risk losing this possibility, then AD may become a reaction to this potential suffering of misrecognition (disrespect). The application of this dimension is demonstrated in Table 2 .

Through this application, a new understanding of intentional academic dishonesty can be formulated. A broader picture can be used to understand that when students engage in AD behaviors; it is not necessarily due to a single factor, an aspect of the individual’s personality, or their gender, field of study, age, or another characteristic. Rather, the universal and basic patterns of recognition that form social life drive students to find ways to achieve the recognition that is needed for positive relation-to-self. The social esteem mode of recognition is particularly applicable; the approval or recognition of the assessor, lecturer, community-of-practice, professional organization, and institution may hinge on the outcome of an assessment or set of assessments. Faced with this struggle, students who do not believe they are capable of achieving this themselves will seek AD behaviors to maintain a chance at recognition, rationalizing this choice if necessary. In other cases, students will seek to rebel against ideologies of assessment as a form of privatized resistance. Sandberg ( 2016 ) identifies that if a group suffers from disrespect or misrecognition, they will strive to regain it. It is in this way that we can reconceptualize AD as the struggle for recognition in the mode of self-esteem. Finally, it can be seen that certain forms of ideology in global society, for instance those based on the merit principle (Herzog, 2016 ) and internalization of performance orientation at all costs (Honneth, 2014 ) are social pathologies, in that they contribute to the instrumentality of education in society.

Implications for teaching, learning, and assessment

This paper has aimed to apply Honneth’s theory of recognition to the practice of AD among students in higher education, through the identification that the self-esteem mode of recognition is at the core of the struggle to succeed in HE to gain recognition and realize a vision of the good life (Sandberg and Kubiak, 2013 ). The stressors of being a student and the societal pressure of completing education as instrumental in achieving the good life, along with the reification of aspects of academia, are then the factors that force the student’s hand in committing AD, and such acts may also have been seen as a form of privatized resistance against perceived issues in a program of study. Current theories are not sufficient to fully explain why this happens, and research on individual and personal variables is conflicting. If AD is reexamined as the struggle for recognition, then there is a firm footing for understanding this phenomenon universally. Furthermore, following this interpretation there is no need for the “moral panic” noted among some faculty in academia (Venera-Mihaela and Mares, 2021 ). In fact, students may be behaving consciously or unconsciously in a rational manner to instinctively protect their self-relation and avoid the destruction of identity, and it is not the case that failing to follow principles of academic integrity is a correlate of a “moral deficit” (Venera-Mihaela and Mares, 2021 ), despite the fact that engaging in such acts entails an act of disrespect to others in itself. Under this interpretation, the causes of AD point to issues within the world of academia, including the reification of academia, the pressures of the student experience, and the stressors of higher education as a struggle for societal recognition. Armed with this understanding, faculty and institutions can do more to understand students’ motivations and work towards corrective action.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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The relationship between academic integrity of online university students and its effects on academic performance and learning quality

Journal of Ethics in Entrepreneurship and Technology

ISSN : 2633-7436

Article publication date: 14 August 2021

Issue publication date: 28 September 2021

This paper aims to investigate the relationship between academic integrity of online university students and its effects on academic performance and learning quality. The first hypothesis aimed to see if there is statistically significant relationship between academic honesty of students taking online classes and their apparent academic performance. The second hypothesis aimed to see if there is a statistically significant difference in academic integrity among male and female students. The third hypothesis aimed to see if there was a statistically significant relationship between academic honesty of students and their quality of learning.

Design/methodology/approach

This is a quantitative study; data was collected via student emails from 155 active online university students.

There was a positive linear relationship for the first hypothesis, the relationship is relatively weak as the value of Pearson correlation was (0.172). For the second hypothesis, the results showed that there was no significant difference between males and females. The results for the third hypothesis showed that there is a statistically significant relationship between academic integrity of students taking online classes and academic learning quality. This relationship is relatively strong.

Research limitations/implications

The sample size may have been a limitation for generalizing the results.

Practical implications

As a practical implication, authors recommend that education administrators focus on training their faculty members to stress and instill strong ethical values, such as academic integrity and honesty, in their students all throughout their academic journey.

Social implications

As for social implication, the embracing of ethical values in students, graduates may continue to embrace such values in the workplace which may lead to more reputable and profitable work environment where the society at large benefits.

Originality/value

This research is among the pioneers that attempted to study the connection of academic integrity and learning quality from the students’ perspective.

• Academic performance
• Online learning
• University students
• Academic honesty
• Academic integrity
• Academic learning quality

Ayoub/Al-Salim, M.I. and Aladwan, K. (2021), "The relationship between academic integrity of online university students and its effects on academic performance and learning quality", Journal of Ethics in Entrepreneurship and Technology , Vol. 1 No. 1, pp. 43-60. https://doi.org/10.1108/JEET-02-2021-0009

Emerald Publishing Limited

Copyright © 2021, Majda I. Ayoub/Al-Salim and Khaled Aladwan.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Traditionally, universities have functioned in a face-to-face setting where students and faculty members meet in a classroom at a pre-arranged time, often on two or three days per week per class. These classes mainly include lectures where students receive information presented by the instructor. While these types of classes are still common at many universities and colleges, current attention has encouraged livelier student involvement in the learning process ( Davies et al. , 2016 ). Depending on the style of the instructor, generally live face-to-face lectures are more interesting and less boring especially if the instructor makes sure to include all students in discussion and participation. The latter stimulates students thinking, makes the class more enjoyable and time seems to pass rather quickly. Many academic articles were aimed to focus on the factors and determinants of quality in higher education. Some concluded that rank of university students for scholarship, additional accomplishments, parents’ level of education as well as age and the institution they are receiving education from, all play a significant role on perceptions about higher education quality ( Akareem and Hossain, 2016 ). Other researchers have found that universities’ education quality differs based on the school itself. It varies based on their size, location, courses they teach, finances available, services and managerial capacity. Only a hand full of universities offer quality education while others are not concerned about the quality; the majority are reliant on part-time faculty members and poor infrastructures with low student satisfaction and students are unsure about their future employability ( Rouf, 2012 ). Other researchers also concluded that instructor feedback and learning style were both significant predictors of high-quality online learning as well as the student satisfaction was a significant predictor of learning outcomes depending on students’ learning styles ( Eom et al. , 2006 ).

Basically, for decades, the focus in higher education had been factors that revolve around the recipient of higher education such as instructors style, feedback and even the infrastructure, but little research attempted to look at the student’s behavior and see if a relationship can be established between the student’s ethical behavior and the learning quality the online university student is receiving. Although online learning for higher education has been in existence for decades, what makes this paper significant is that it aimed to focus on the level of academic integrity of university students while using online platforms. Also, this research aimed to see the effect of the online academic integrity of students and its relationship with the learning quality the students are receiving. Also, the researchers will be exploring how academic integrity of university students is related to apparent academic performance such as student grades. The researchers only focused on university students in the country of Jordan.

Literature review

The following is a review of the literature with respect to the variables in this research. The researchers will be touching on academic integrity of university students, discussing published literature with respect to the online university students and how misconduct usually occurs as students submit their assignments, projects, presentations and online assessments. This review will also include literature regarding academic performance of the online university students and true academic learning.

Academic integrity of students

Academic honesty and integrity are considered as the core value in our universities for learning, teaching and all academic activities. Nevertheless, the academic literature contains countless reports that proposed plagiarizing and cheating by students have increased in recent years ( Osika, 2009 ). The available and ready technologies have contributed to students’ behaviors going from cutting and pasting materials from the internet to sharing online quizzes and texting answers to classmates ( Piascik and Brazeau, 2010 ).

Academic honesty represents a high priority for all universities and educational environments, it is principally of concern in courses offered online where students work independently and with less direct monitoring of their actions by their instructors. Several studies showed that students and instructors tend to believe that academic dishonesty and cheating happen more likely in an online environment which reflects the students' morals and principles ( McGee, 2013 ; Dietz-Uhler and Hurn, 2010 ; Cluskey et al. , 2011 ). Additionally, cultural differences that are unseen to the system may encourage academic dishonesty for example, when students from non-Western cultures are expected to know and operate based on Western values of behavior, those students become at a disadvantage ( McGee, 2013 ). Payan et al. (2010) revealed that collectivist business students are more tolerant than individualist business students concerning questionable academic behaviors. Nevertheless, academic dishonesty may lead to future immoral conduct in the workplace. There is research that connects academic dishonesty among students with future unethical behavior in the workplace; international students were stated to be a largely susceptible group ( Brown et al. , 2019 ).

Online class lectures

The online learning has its own benefits and limitations; among the main benefits according to Appanna (2008) from the student’s perspective is the flexibility, as it offers value to many working adults trying to balance work, family and study requirements. Appanna also added that the lack of visual cues may permit the instructor to treat all students equally. However, there are many limitations for the online synchronous learning such as students experiencing class disruption as the class remains open to students and the online students may sign on or sign off at any time during a class session. Additionally, students with learning disabilities or with language limitations may be irritated by the fast pace and text-based environment ( Appanna, 2008 ). Moreover, if instructors rely too heavily on multiple choice and true-false questions on exams, it may not be adequate to assess students’ depth of knowledge and understanding and their ability to give thorough answers as in long essay questions ( Appanna, 2008 ). The synchronous education participation requires students to have dynamic involvement and collaboration, and some students may not be as social. Therefore, in a synchronous learning environment some students may find it a challenge to join in online live discussions ( Chauhan, 2017 ).

Many online instructors maybe required to record their live lectures and have them available for students to retrieve at any time during the course. Kuznekoff (2020) concluded that the online lecture videos that students have access to have an effect on students’ learning; however, as students spend more time watching videos, there was a reduced student learning. Kuznekoff also found that only 34% of students viewed the full lecture video, 40% watched part of the lecture video. Moreover, the average viewer watched less than 60% of each recorded video used in online classes ( Kuznekoff, 2020 ). Fita et al. (2016) concluded that synchronous virtual e-learning is a beneficial tool for tutoring, for cooperative work both in the physical classroom and at home for remote learning. However, any new teaching tool used demands adaptability to the newer methods of teaching; therefore, more research must be conducted to evaluate the learning quality and enhance the benefits using new tools ( Fita et al. , 2016 ).

Assignments/projects/presentations

Kwong et al. (2010) conducted a study in Hong Kong and revealed that the perception of academic honesty such as the seriousness of plagiarism with respect to students may be different from faculty members. Normally students seek misconduct due to different reasons such as course overload which may create tremendous pressure on students ( Kwong et al. , 2010 ). Another research by Tabsh et al. (2017) aimed to address university students’ and faculty’s perceptions of plagiarism incidents, unauthorized collaborative work and copyright abuses. Faculty members suggested enforcing tougher consequences, educating students on academic honesty and assigning more proctors during exams to minimize academic integrity violations. However, both students and faculty members suggested that educating students on academic integrity and using more proctors, as well as practicing more lenient assignment deadlines and offering easier examinations maybe an option as well ( Tabsh et al. , 2017 ). Many universities offer software such as Turnitin to detect work that had been plagiarized for course assignments. McGee (2013) argues that some researchers claim that academic dishonesty could potentially be a result of course design. Many studies have been conducted attempting to find ways to improve academic honesty in e-learning for example, Amigud et al. (2017) tested a technique that aligns students’ identities with the work they submit through examining patterns in their submitted assignments. Analytics allows identity automation and is able to assure authorship and resorting for an instructor only in cases where human interference is essential; this may produce more convenience, competence, honesty and integrity in the process of evaluating students’ submitted work ( Amigud et al. , 2017 ).

Online exams

According to Swartz and Cole (2013) , the academic institution has the main responsibility for upholding academic honesty in the classroom whether online or face-to-face, by creating a culture that will not tolerate academic dishonesty. Instructors should be supported by management to impose tough consequences for those who violate, and instructors should stress the values of ethics in the learning environment, as students will be applying such ethical values in the workplace. However, it is a fact that when a student wants to cheat, he/she will figure out a way as cheating will never be eradicated but can be minimized ( Swartz and Cole, 2013 ). Bearman et al. (2020) indicated that academic integrity concentrates on preparing students with the competences and morals essential to conduct ethical scholarship. On the other hand, exams’ security concentrates on toughening exams to counter cheating efforts and attempts to spot any cheating that has happened. Although different objectives yet essential to make sure that students who complete their bachelor’s degrees have met the desirable outcomes ( Bearman et al. , 2020 ). According to O’Connell (2016) , the present philosophy toward academic integrity is affected by policy activities, such as syllabi or course outlines emphasizing academic integrity and ethical rules or conduct. Academic integrity of students is also affected by the ability of academics to teach as well as enhance the students’ knowledge and acceptance of academic integrity standards ( O’Connell, 2016 ).

The question remains; will we ever know who had taken the online assessment at home? Is it the actual student or someone else? Universities in search of conformance to the Higher Education Acts, according to Lee-Post and Hapke (2017) , are advised to put in place an end-user verification solution that may authenticate a learner’s identity, legitimacy and presence. Technology is improving so swiftly where universities may implement cost-effective solutions to enhance the academic integrity using sophisticated yet inexpensive authentication software and hardware. Such solutions should integrate both deterrence and implementation approaches to sufficiently address academic integrity ( Lee-Post and Hapke, 2017 ). Also, some studies were conducted to compare students’ performance when being watched compared to not being watched while being examined in online classes. Daffin and Jones (2018) concluded that students performed noticeably worse on proctored examinations than non-proctored examinations. The difference in performance maybe due to students’ overall nervousness regarding taking the exam and is worsened by being observed during the exam. Or it is maybe due to the fact that students are unable to resort to their notes, internet sources, other students and/or their textbook, which they may potentially resort to in a non-proctored examination regardless of directions forbidding them from resorting to such resources ( Daffin and Jones, 2018 ). There are other creative ways where the integrity of the online exams can be secured using technology. For example, Traoré et al. (2017) concluded that continuous authentication is a new technology that can be suitable in tackling a diversity of security issues. The face recognition system may record the examinations, does face recognition verifications for all students instantly and produce alarms that alert exam proctors. Such system can detect different forms of prohibited identity sharing as well as cheating on online examinations ( Traoré et al. , 2017 ).

Academic performance versus true academic learning

It is a captivating paradox in education: students can be excitedly successful on tasks and responsibilities in class but learn virtually nothing; contrariwise, students can do somewhat poorly on those same tasks but learn and absorb quite a lot ( Soderstrom and Bjork, 2015 ).

Such scenarios and situations demonstrate one of the most substantial differences in all memory and human learning literature, namely, the difference between performance and learning. Performance denotes temporary variations in behavior or knowledge that can be observed or measured during, or shortly after, instruction. Learning, on the other hand, refers to somewhat permanent changes and development in behavior or knowledge. It is, or at least should be the goal of education ( Soderstrom and Bjork, 2015 ). Consequently, academic dishonesty may lead to future immoral conduct in the workplace. There is research that connects academic dishonesty among students with future unethical behavior in the workplace; international students were stated to be largely susceptible group ( Brown et al. , 2019 ).

Altogether, learning is considered as a long-running process, whereas, performance is short-term. However, this means that instructors will not distinguish if their students have acquired or learned something up until after a while in which the students did not use or think about the information ( Soderstrom and Bjork, 2015 ). Because of COVID-19 pandemic, most universities, instructors and students unexpectedly find themselves enforced to switch from physical classroom teaching and learning into using remote learning technology. Increasingly, this transformation raises tons of issues, from internet connectivity and coverage, students’ questions and queries, how instructors and universities handle students’ grades to how universities tackle students’ evaluations of instructors. Moreover, this leads to the following question: What impact does this emergency immersion into online learning by many universities may have on faculty and students’ confidence in technology-enabled learning ( Lederman, 2020 ). The above arguments and question have been supported by the results of a study conducted by Thompson et al. (2017) which demonstrated that true academic learning and academic honesty are fully achieved through cult-learning in physical face to face classrooms. This current research asked respondents about their final grades as well as their cumulative grade point average (GPA) as a measure for their academic performance. Furthermore, the researchers aimed to examine if there is a relationship between true academic learning and academic integrity of online university students.

Methodology

The main fieldwork of the current research was conducted in Jordan that lasted a couple of months. This research addresses the gap in research concerning the relationship between the academic integrity of online university students and their true learning quality as there is shortage of such studies. Prior to the actual data collection, a draft version of the questionnaire was pilot tested, involving a sample of 27 students to obtain their perceptions and comments on the questionnaire’s design and the wording of questions. The questionnaire consisted of Likert-scale questions with five options to choose from beginning with strongly disagree and ending with strongly agree. Following the pilot test, questionnaires were distributed electronically to students’ emails sent by the deanship of student affairs, to guarantee that these questionnaires only would be filled by university students. The questionnaire was sent to few local universities’ students, google forms were used for collecting the data for enhanced accuracy of data entry. Anonymity was guaranteed and no data was collected that could identify respondents.

The primary data was collected by using a highly structured quantitative instrument, that is, a self-administered questionnaire, the questionnaire was sent via email to 1,500 active undergraduate students enrolled in local universities; 155 filled out questionnaires with a response rate or sample size to be about 10.33%. There were 97 females and 58 males who filled out the questionnaire; 120 (77.9%) were Jordanians or Palestinians, 21 (13.6%) were from the Middle East and North Africa. The primary data in the form of excel sheet had been numerically coded and accurately entered a statistical package for social sciences (SPSS version 26). Additionally, the current research had employed the quantitative methods to support the testing of the present concepts and theory which are comprised of different sets of research variables (academic integrity and honesty, academic performance, and quality of learning). It can be argued that quantitative methods are related to hypotheses testing ( Collis and Hussey, 2014 ; Lewis et al. , 2012 ). Moreover, the validity concerning the theoretical generalization can be established by using quantitative methods, and this is widespread in the field of social sciences and management studies ( Neuman, 2014 ).

The questionnaire consisted of two parts; the first part was designed to measure the demographic characteristics, which includes gender, age and nationality and so on. To measure the Academic Performance variable, the researchers examined previous studies conducted in this perspective. Many studies specify that course grades and GPA are the most used tools to measure academic performance ( York, 2015 ). Additionally, according to Huang (2011) , we can measure academic performance via measuring self-efficacy; which led the authors of this research to add questions enquiring respondents to rate their effective online class participations or class discussions. Also, the respondents were asked to rate the quality of their research papers whether they have gotten better or worse since the switch to online learning ( Huang, 2011 ). Moreover, presentation skills are among the important factors that have been used to measure academic performance of university students; that’s why the authors of this paper added a question on the survey to rate the students’ presentation skills, whether they have been enhanced as they take courses online ( Shahzadi and Ahmad, 2011 ).

There is a statistically significant relationship between academic honesty of students taking online classes and their apparent academic performance.

There is a statistically significant difference in academic integrity among online males and females.

There is a statistically significant relationship between academic honesty of students taking online classes and their learning quality.

The research questionnaire was distributed to a random sample of 27 enrolled undergraduate students that had taken at least one class online to make sure that the statements are understandable; the language is simple and clear. Also, to make sure that the nature of the students’ response to the statements does fit the intended meaning and the questionnaire measures what it’s supposed to measure. Necessary tests were also carried out and necessary modifications have taken place based on the results obtained, the final version was tested again as demonstrated in the following sections.

Face validity

The validity of the research instrument was analyzed using face validity, construct validity and discriminant validity as well as other procedures to ensure the soundness of this research. For face validity, the researchers presented the questionnaire to a panel of experts with a total of five experts from different universities who are specialized in the field of education. The views and opinions of the panel were seriously considered, the required adjustments were made based on their suggestions and comments.

Construct validity

Construct validity was analyzed using the correlation coefficients between items of the questionnaire variables, the results are presented in Table 1 .

Table 1 indicates the correlation that expresses the construct validity among the questionnaire items. The highest value of correlation that could be reached is (1), so a minimum value of 0.40 is considered good and acceptable correlation value ( Laher, 2010 ). Inspecting the provided values in Table 1 , it is clear that all the mentioned correlation values were > 0.40 in all factors suggesting good construct validity for each variable expressed by its related items.

Discriminant validity

Discriminant validity refers to the extent to which factors are distinct and uncorrelated.

Before assessing the discriminant validity KMO and Bartlett's tests were performed, the results are presented in Table 2 .

Table 2 shows the results of KMO and Bartlett's Tests including Chi-Square. KMO results of the measurement adequacy (which determines if the responses given with the statements are adequate or not), value of 0.854 are greater than cutoff point of 0.5 and therefore considered acceptable. Indicating that the data are suitable for structure detection.

The value of Chi-Square (1395.403) is greater than the tabulated value at the degree of freedom of 190 which equals to 124 at α ≤ 0.05 indicating that the data is suitable for analyses. In addition, Bartlett’s test of sphericity is significant (0.000 less than 0.05) which means that correlation matrix is not an identity matrix ( Cerny and Kaiser, 1977 ).

Reliability analysis

Cronbach's alpha test was used to assess the reliability of the research instrument. Table 3 shows results for the 20 statements of the questionnaire and how closely related a set of items are as a group over the sample of respondents.

Table 3 shows that all factors Cronbach's alpha values are acceptable. The overall value is (0.894) indicating high level of reliability of the questionnaire reflecting relatively high internal consistency, since the reliability coefficient of 0.70 or higher is considered “acceptable” in the majority of social science research situations ( Nunnally, 1978 ).

Descriptive analyses

Below, the descriptive analysis of the research tool is presented. The researchers relied on the following scale to describe the mean values based on the following equations:

(Highest weight “6” – Lowest weight “1”)

Category length = -----------------

No. of categories “6”

Category length = --- = 0.83

Accordingly, the researchers relied on the following scale to describe the mean values

1.00 – 1.83very low

1.84 – 2.67low

2.68 – 3.51slightly low

3.52 – 4.35slightly high

4.36 – 5.19high

5.20 – 6.00very high

Descriptive statistics for the variables

Analyzing the items of the variables, Means, standard deviations, and mean indices (MI) were calculated for each item, as demonstrated in the following sections ( Figure 1 ).

Table 4 indicates connection problems or computer issues if I see myself running late in turning in assignment on time” factor recorded the highest mean among the factors being rated by the study sample, as it ranked the first with a mean of (2.32), while the statement no. 1 “I always resort to someone to do part or all of the work for me with my online assignments” was the least factor that was addressed as it recorded the least mean (1.79). The overall assessment degree of the Academic Integrity of the online learning students factor was rated by a mean of (2.0839). This value expresses a low level of agreement among the study sample. The values of means and standard deviations, MI for the Academic Integrity of the online learning students. The statement no. 3 “I can always resort to internet ( Figure 2 ).

Table 5 indicates the values of means and standard deviations, MI for the Academic Performance of the online learning students. The statement no. 2 “I have been participating more during online class discussions compared to on campus” factor recorded the highest mean among the factors being rated by the study sample, as it ranked the first with a mean of (3.21), while the statement no. 4 “The scores of my projects and presentations have improved noticeably since we switched to online learning” was the least factor that was addressed as it recorded the least mean (2.86). The overall assessment degree of Academic Performance of the online learning students factor was rated by a mean of (3.0145). This value expresses a slightly low level of agreement among the study sample. ( Figure 3 ).

Table 6 indicates the values of means and standard deviation, MI for the Academic learning quality of the online learning students. The statement no. 4 “On campus lectures are much better suited than online to building my skills, knowledge and abilities for my future career” factor recorded the highest mean among the factors being rated by the study sample, as it ranked the first with a mean of (5.36), expresses a very high level of agreement among the study sample, statement no. 2 “Online Lectures are boring and difficult to learn from” factor ranked in the second place with a mean of (4.46) expresses a high level of agreement among the study sample, while the statement no. 1 “I can always resort to someone to help me with the online exams which usually enhances my final grade” was the least factor that was addressed, as it recorded the least mean (2.05), expresses a low level of agreement among the study sample. The overall assessment degree of the factor Academic learning quality of the online learning students was rated by a mean of (3.485). This value expresses a slightly low level of agreement among the study sample.

Hypotheses testing

Before testing the hypotheses, two basic assumptions must be tested to apply linear regression; normality of the data distribution of the independent variables and the level of multicollinearity among the independent variables. Skewness and Kurtosis test were used for normality, and VIF (Variance Inflation Factor test) was used for multicollinearity, the results are presented in Table 7 .

Table 7 shows the skewness and Kurtosis values and the VIF results. Data are considered to be close to the normal distribution if it lies between (−3 and +3) ( George and Mallery, 2002 ). The obtained values proved that the data is normally distributed as all obtained values lie between assigned range. Maddala (1992) mentioned that a value of VIF more than 30 is considered to be a big problem, a value more than 10 leads to untrusted with the coefficients, a value between 5–10 reflects a moderate problem, while a value less than 5 reflects no real issue. All obtained values were less than 2, which means that there is no multicollinearity problem between variables.

Based on the above results, testing the hypotheses can be carried on.

Testing the first hypothesis

According H1 , there is a statistically significant relationship between Academic Integrity of students taking online classes and their Academic Performance. Gender as a moderation effect on the original relationship between Academic Integrity of students taking online classes and their Academic Performance.

Table 8 shows the relationship between Academic Integrity and Academic Performance directly and then under with Gender interaction, the first Model has a positive linear relationship, the relationship is relatively weak as the value of Pearson correlation is equal to (0.172) and sig. value (0.032) is less than 0.05 ( Cohen, 1988 ).

Since the sig. F value is (0.000) which is less than (0.05), the alternative hypothesis, H1 is accepted, indicating that there is a statistically significant relationship between academic Integrity of students taking online classes and their Academic Performance. This relationship is relatively weak.

In the second Model the Gender interaction has insignificant relationship with Academic Integrity and Academic Performance, the value of Pearson correlation (R) is equal to (-0.086) while the sig. value (0.288) is greater than 0.05. Also, the change in R Squared value is equal to 0.001, which means that the ability of Gender interaction to explain the variation in the relationships between Academic Integrity of students taking online classes and their Academic Performance did not significantly change. The sig.t ( p -value) is equal to (0.6930) which is greater than 0.05, accordingly, is rejected as there is no statistically significant effect of the demographic variable Gender on the original relationships between Academic Integrity of students taking online classes and their Academic Performance.

Testing the second hypothesis

There is a statistically significant relationship between Academic Integrity of students taking online classes and the Academic Learning Quality. Gender has a moderation effect on the original relationship between Academic Integrity of students taking online classes and their Academic Performance.

Table 9 shows the relationship between Academic Integrity and Academic Learning Quality directly and then under with Gender interaction. The first Model has a significant strong positive linear relationship, where the value of Pearson correlation is equal to (0.571) and the sig. value (0.000) is less than 0.05 ( Cohen, 1988 ). Since the sig. F value is (0.000) which is less than (0.05), the alternative hypothesis, is accepted, which means that there is a statistically significant relationship between academic Integrity of students taking online classes and Academic Learning Quality. This relationship is relatively strong.

In the second Model, the Gender interaction has insignificant relationship with Academic Integrity and Academic Learning Quality, the value of Pearson correlation (R) is equal to (0.044) while the sig. value (0.583) is greater than 0.05. Also, the change in R Squared value is equal to 0.003, which means that the ability of Gender interaction to explain the variation in the relationships between Academic Integrity of students taking online classes and their Academic Learning Quality did not significantly change. The sig. t ( p -value) is equal to (0.406) which is greater than 0.05, accordingly, the alternative hypothesis is rejected whereas there is no statistically significant effect of the demographic variable Gender on the original relationships between Academic Integrity of students taking online classes and their Academic Learning Quality.

Discussion and conclusion

The descriptive data for this study indicated that the statement on the survey that addressed academic integrity of the online learning students had the highest mean, as follows: “I can always resort to internet connection problems or computer issues if I see myself running late in turning in assignment on time.” Almost 15.5% of students agreed and strongly agreed with this statement. Evidently, these students admitted that they would resort to dishonest excuses to lessen the consequences of such behavior.

Also, according to the descriptive data for this study, it indicated that the statement that addressed Academic Performance of the online learning students, had the highest mean, the statement of the survey was as follows; “I have been participating more during online class discussions compared to on campus”; 29% of respondents agreed and strongly agreed with the statement. This can be a pointer, which could mean that those students who were relatively shy to participate in face to face class discussions were encouraged more to participate in the online synchronous environment, since the online camera was never mandatory to be on for students.

Additionally, according to this study’s descriptive data, the statement that addressed Academic Learning quality of the online learning students which had the highest mean, the statement of the survey as follows; “On campus lectures are much better suited than online to building my skills, knowledge and abilities for my future career”. Approximately 85% of respondents agreed and strongly agreed with this statement. It seems that students put much more faith in the face-to-face learning environment as opposed to online learning environment. One potential reason for such overwhelming response is that the online learning environment is very new in the region and is still frowned upon by parents and faculty members.

While the statement in this category that says, “Online Lectures are boring and difficult to learn from” scored the second highest in this category and slightly over 56% of respondents agreed and strongly agreed with this statement. Seemingly, more than half the respondents believed that online lectures are not as exciting as face to face lectures and are difficult to learn from. Additionally, in this same category the statement that says, “I feel the quality of education I am receiving had suffered tremendously since we started taking classes online” 46% of respondents agree or strongly agree with this statement; this can be an alarming finding that almost half the respondents perceived that the online education was not of the expected quality. The latter result maybe contributed to the fact that the online line learning platform was imposed on all due to the COVID19 pandemic, and for some; it may not have been the optimal way of receiving education.

In conclusion, Academic Integrity of students taking online classes does affect Academic Performance in a weak way, which means that there are more important factors that affect the Academic Performance of students. Both male and female students answered the same way, as gender did not make any difference in this result.

Here are some pointers for the readers to think about is that the result of the first hypothesis does not mean that if students are honest academically then their academic performance will be high; however, this may mean that some students who are academically honest may or may not perform well academically depending on how much effort they put into the course and other individual factors such as IQ levels or their interest in the topic. However, some students who are not academically honest yet may perform very well on online tests, projects and other means of evaluation which is not a valid indicator of quality of learning.

A formal education must be shared with students on the importance of academic integrity; for such education to be successful there must be collaboration between faculty members, staff as well as students ( Garza Mitchell and Parnther, 2018 ).

The relationship between Academic Integrity and Academic Learning Quality was measured in the third hypothesis which means that there is a statistically significant relationship between academic Integrity of students taking online classes and Academic Learning Quality. This relationship is relatively strong (0.57) The Gender interaction has insignificant relationship with Academic Integrity and Academic Learning Quality which means that the higher the academic integrity of online students, the higher their learning quality would be. This brings the researchers to point out the idea that when more students are practicing academic integrity, the more the assessments of their performance are accurate and more indicative of their true knowledge and skills. Additionally, The researchers’ last pointer for readers to think about; which is how online education divisions would distinguish themselves from competition, and how universities can market their online education in a way that would offer a competitive advantage and still allow them to stand behind their claims of offering high quality online education where it truly is building the skill, knowledge and abilities of online students.

The study established that there is a difference between academic performance and academic learning which is in agreement with Soderstrom and Bjork (2015) mentioned in the literature review where learning and its quality should be the focus of educators and not performance as learning carries on for a long time. Therefore, the researchers conclude that educators need to aggressively educate online university students about the importance of academic honesty since it was concluded in this study that academic honesty or integrity is directly and strongly related to the learning quality. The latter conclusion is in agreement with ( Bearman et al. 2020 ); O’Connell, 2016 ; Tabsh et al. , 2017 ) as mentioned in the literature review. The use of technology may minimize academic dishonesty and could enhance the learning quality through the assurance of authorship via different means ( Amigud et al. , 2017 ; Lee-Post and Hapke, 2017 ; Traoré et al. , 2017 ). What is more important here however, is for education administrators to focus on training their faculty members to be stressing and instilling strong ethical values such as academic integrity/honesty, in their students from the very beginning and all throughout their academic journey. Once such ethical values are embraced, graduates may continue to embrace such values in the workplace. The work environment becomes much healthier when it is crowded with high integrity employees and leaders which may lead to better reputation of the organization they work for, and ultimately more sustainable profits and societies, at large, benefit.

The data was collected from 155 students which is not necessarily a very large sample that may have resulted in a type II error or may have reduced the strength of the study which can be a potential limitation. Additionally, due to the quantitative nature of this research, it lacked depth however, for future research it is recommended to use interviews with online university students to get more in depth responses as to the effectiveness in gaining knowledge, desired workplace skills and abilities, and how academic integrity of online students, from the students standpoint, can be improved.

Academic integrity of the online learning students

Academic performance of the online learning students

Analyzing the items of academic learning quality of the online learning students

Correlation coefficients between the item and its total for each variable

KMO and Bartlett's tests

Reliability analysis through Cronbach alpha results

Means, SD and MI for academic integrity of the online learning students arranged in a descending order

Multiple linear regression for academic integrity of students and their academic performance along with gender interaction

Multiple linear regression for academic integrity of students and the academic learning quality along with gender interaction

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Academic Dishonesty or Academic Integrity? Using Natural Language Processing (NLP) Techniques to Investigate Positive Integrity in Academic Integrity Research

• Open access
• Published: 05 June 2021
• Volume 19 , pages 363–383, ( 2021 )

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• Thomas Lancaster   ORCID: orcid.org/0000-0002-1534-7547 1  

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Is academic integrity research presented from a positive integrity standpoint? This paper uses Natural Language Processing (NLP) techniques to explore a data set of 8,507 academic integrity papers published between 1904 and 2019.

Two main techniques are used to linguistically examine paper titles: (1) bigram (word pair) analysis and (2) sentiment analysis. The analysis sees the three main bigrams used in paper titles as being “academic integrity” (2.38%), “academic dishonesty” (2.06%) and “plagiarism detection” (1.05%). When only highly cited papers are considered, negative integrity bigrams dominate positive integrity bigrams. For example, the 100 most cited academic integrity papers of all time are three times more likely to have “academic dishonesty” included in their titles than “academic integrity”. Similarly, sentiment analysis sees negative sentiment outperforming positive sentiment in the most cited papers.

The history of academic integrity research is seen to place the field at a disadvantage due to negative portrayals of integrity. Despite this, analysis shows that change towards positive integrity is possible. The titles of papers by the ten most prolific academic integrity researchers are found to use positive terminology in more cases that not. This suggests an approach for emerging academic integrity researchers to model themselves after.

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Academic integrity research has been published dating back to at least to the 1900s. Academic integrity publications span disciplines and research is published under a variety of different themes. Barnes ( 1904 ) published the paper “ Student honor: A study in cheating ”, providing an early example of using surveys to conduct qualitative and quantitative academic integrity research. Although Barnes’ research may have not been presented with the rigour of some more recent academic integrity investigations, it still identified themes that are commonly discussed in the field today.

In Barnes ( 1904 ) students were asked how they would respond to an academic integrity scenario that had occurred in real life and for which a variety of academic penalties were available. In the scenario, examination questions were said to have been stolen, giving the students who had to access to them an unfair advantage over their peers. The responses received included disparate opinions from students regarding whether it was their business to get involved further. Thirty percent of male students and 35% of female students felt that reporting was necessary so that they would not be unfairly judged against other students who they now expected would benefit from better results. Barnes also noted differences in responses between genders, a theme which is still regularly investigated as part of academic integrity research to this day.

Barnes' choice of paper title does rather seem to present their own position on academic integrity issues. This is perhaps most clearly summed up in this quote from the paper:

“The reasons are mainly selfish; the university's interests are far less important than self-protection; while general social responsibility is comparatively little felt.”

Despite being only six words long, the paper title “ Student honor: A study in cheating ” brings together two words at different ends of the academic integrity spectrum, honor and cheating . The word honor carries with it an expectation that students will act with positive integrity. The word cheating has negative connotations, with the suggestion of students getting an unfair advantage. A focus on transgressive behaviour is not necessarily wrong, but also leads to missed opportunities for people working in the academic integrity field. The conflict between whether academic integrity should be framed in a positive or negative manner still exists in paper titles today and is the focal point of the research investigation presented in this paper.

This paper uses Natural Language Processing (NLP) techniques to provide a data-driven investigation into how academic integrity paper titles have been constructed between 1904 and 2019. The research presented examines the titles of 8,507 papers published in the wider academic integrity field and is used to see how far such titles are presented to readers using a positive or negative approach. The results are intended to help the academic integrity research field to determine if it wishes to present itself from a more positive direction.

Academic Integrity

The literature on academic integrity often considers this field through both positive and negative viewpoints, with integrity itself considered as a positive term. A look at the different opinions and presentations of this research is useful to help define how the field is changing, as well as to allow positive integrity and negative integrity ideas to be demonstrated through representative examples.

The popularisation of the term academic integrity is commonly attributed to McCabe. Despite this, in the single most highly cited paper in the field “ Academic dishonesty: honor codes and other contextual influences ”, McCabe and Trevino ( 1993 ) do not discuss academic integrity, but instead academic dishonesty. In the paper, McCabe and Trevino collected data using a survey methodology and discussed how this could be used to predict academic dishonesty. Despite its high citation level, the focus of both the paper title and content brings connotations of a negative presentation of integrity.

Similar observations to those made about McCabe and Trevino ( 1993 ) also appear in a literature review by Macfarlane et al. ( 2014 ). They examined 115 articles in the field across both Western and Chinese literature. Their review concluded that academic integrity is commonly defined by reference to misconduct, fraud and corruption. This paper will consider research with a focus on areas such as these as being representative of negative integrity.

An alternative group of approaches are possible. This paper will consider such approaches as representative of positive integrity, often represented by the pure term academic integrity. Macfarlane et al. ( 2014 ) define academic integrity as “ the values, behaviour and conduct of academics in all aspects of their practice ”. An alternative definition, given by East and Donnelly ( 2012 ) based on the values of the institution they work for is “ academic integrity means being honest in academic work and taking responsibility ”. That interpretation is close to how sector organisation the International Centre for Academic Integrity (ICAI) present this. ICAI take a positive integrity view and define this concept in terms of core values by asking members to commit to “ honesty, trust, fairness, respect and responsibility ” (Fishman, 2014 ).

Fishman ( 2016 ) discusses the variety of frameworks which academic integrity is presented under in the United States. These include moral and ethical frameworks, pedagogical frameworks, legalistic frameworks, comparing academic integrity with criminal behaviour and even considering this as a form of disease. Although these frameworks provide some opportunity for a positive discussion, the most immediate interpretation is that academic integrity should be viewed through a negative lens.

There have been opportunities for the negative viewpoint to change. McCabe and Pavela ( 2004 ) discuss principles they believe will help build a culture of academic integrity, such as making this an institutional value with consistent standards, clarifying expectations with students, enabling students to take responsibility and ensuring fair assessment. How academic integrity principles are taught to students and how far teaching can take a positive approach continues to be an important part of the modern discussion (Ransome & Newton, 2018 ; Sefcik et al., 2020 ).

One underlying principle regarding making academic integrity work at an institutional level is that it should apply to the whole academic community, not just to students and not just to academics. The student voice is being increasingly considered as an essential and important part of this discussion (Pitt et al.,  2020 ).

The fields of research studied within academic integrity have widened in recent years, with new areas developing as a result of observing threats to academic integrity. Some identified challenges include cybersecurity threats (Dawson, 2020 ), contract cheating (Clarke & Lancaster, 2006 ), study helper websites (Harrison et al., 2020 ) and paraphrasing tools (Prentice & Kinden, 2018 ). The positioning of research discussing threats to integrity and opportunities for student misconduct suggest a continuing view of negative integrity. The fast pace of technological change and the need to raise awareness of this further suggest that a certain level of negative integrity research will always be required within the field.

The widening of the academic integrity research field and the growth of technology has brought with it the opportunity for innovation in how academic integrity research is conducted. Methodologies have moved beyond surveys. Social media analysis can be used to investigate why students cheat (Amigud & Lancaster, 2019 ). Region and sector specific literature reviews are possible (Eaton & Edino, 2018 ). Internal academic conduct records can be analysed (Atkinson et al., 2019 ). Others have had success working around analysing existing policies (Eaton et al., 2020 ). There is plenty of alternative data available that can be examined.

This paper takes such an alternative and data-driven research approach. It considers existing data relating to published academic integrity research and uses NLP techniques to programmatically examine this data.

For the purpose of this paper, the view that academic integrity applies to everyone is supported, but this is balanced by the observation that papers are most relevant if they fit within an educational setting. As such, the interpretation considers academic integrity as it applies to teaching, learning, pedagogy and education, where students, academics and professional university staff are at the forefront of the conversation. The related field of research integrity is sometimes included with academic integrity, but to award muddying the water it is only included in the investigations reported here when this also relates to education.

Although this paper makes no attempt to provide a fresh definition of academic integrity, the approach taken naturally identifies papers with examples of both positive integrity and negative integrity. One side product of looking at both positive and negative views is that it is hoped the range of papers, topics and issues identified will help to inform future definitions of academic integrity so they can both be current and complete.

Investigative Methodology

Formation of the primary data set.

The research presented in this paper relies on a data-driven approach. Data was collected in May 2020 to form a primary data set. From this four further secondary data sets were derived.

The procedure through which the data sets were gathered and processed employed standard techniques from the domains of NLP and machine learning. As is customary in this field, experimentation was undertaken on the initial data to determine how best to present it for NLP. Some of the final decisions presented here may appear arbitrary, but they were made to fine tune the results for readability and accuracy. The pipeline is presented to provide enough information for researchers looking to undertake related studies, whilst recognising this paper is aimed at the academic integrity field, an audience who may be unfamiliar with NLP.

Google Scholar was used as the primary data source to identify academic integrity research publications. Data was collected from Google Scholar through an iterative process, with the aim of ensuring data set completeness and consistency. Both manual and automated checks and corrections were made on the resulting data. Excel and Python were used extensively to support data collection and processing with several scripts developed for internal use. The NLP aspects of processing relied heavily on the NLTK platform. Sentiment analysis, a process where the subjective information in a written expression is evaluated to identify the tone of the expression, was completed used the VADER toolkit. Both NLTK and VADER are open source.

Figure  1 provides a high-level overview of the data collection, cleansing and processing pipeline.

Data set formation pipeline

As Fig.  1 indicates, an initial set of search terms for Google Scholar were identified. These included such terms as academic integrity , academic dishonesty, student plagiarism and contract cheating . In each case, a search for these terms in the title of documents was conducted. This search type meant that a term like student plagiarism would match the word student and the word plagiarism used anywhere within a title and with the words in either order. The results were manually inspected to identify other possible search terms. Subsequently a list of bigrams (two consecutive words) in the titles was generated to identify more possible search terms. Frequently occurring bigrams related to the wider academic integrity area were also used. This process identified, for example, the term academic honesty as an alternative to academic dishonesty. The process also suggested the term research integrity , but this was deliberately excluded to avoid adding large quantities of papers to the initial data set that were unrelated to teaching, education or students. Nevertheless, some research integrity papers do appear in the final evaluation where these were identified through other terms and did prove to be relevant to academic integrity. Table 1 shows the final set of search terms that were used.

The initial data set required extensive cleansing. The process which Google Scholar uses to crawl research papers and generate its own records is error prone and so the initial data set contained many duplicate entries, for example where one version had a slightly incorrect title, listed authors in different orders or had author name variants. There were many cases where unsuccessful parsing of research documents had generated incorrect information in the Google Scholar database. In addition, information standing out as potentially suspect was cross-referenced against other sources. One such example was an article about student cheating and the Internet, allegedly published in 1970, whereas a check on the journal’s own web pages revealed the correct date.

A further pruning process was necessary to limit the initial data set to only include papers that were research related and on subjects in the wider academic field. Table 2 summarises some of the main criteria applied to identify if papers should be included in or excluded from the primary data set. No direct attempt was made to judge the quality of the papers or exclude those published in predatory journals, although the development of secondary data sets of papers that the academic integrity community considers most important does indirectly address this possible limitation.

The primary data set contained information about 8,507 research sources published between 1904 and 2019. A cumulative frequency graph showing when the papers were published in shown in Fig.  2 .

Cumulative frequency chart of academic integrity paper publications

Figure  2 indicates that the rate of increase of publications in the academic integrity field has been exponential. There were approximately the same number of papers published between 1904 and 2011 as there were between 2012 and 2019. Although this may seem like a steep rate of increase, worldwide science and engineering publications were found to have grown at a rate of 4% per year between 2008 and 2018 (White, 2019 ). The corresponding figure for academic integrity publications is just below 3% per year.

Formation of the Secondary Data Sets

A further four smaller secondary data sets, all subsets of the primary data set, were developed to allow for a more detailed investigation. These data sets are summarised in Table 3 .

Data sets B, C and D consider the most cited papers of all time. These are intended to represent the papers that have overall influence on the academic integrity field. Although these data sets would seem to favour older publications, the 1000 most cited papers data set (D) does contain papers dated as recently as 2019. In general, the primary data set (A) is where most recently published research can be found. This is illustrated in Fig.  3 , which shows the relative cumulative frequencies of publications in the five data sets, truncated to start from 1979.

Relative cumulative frequencies of paper publications in data sets (1979 to 2019)

Computation of Individual Primary and Secondary Data Set Records

Individual records were generated for each paper included in the data sets through a combination of continued data cleansing and the application of NLP techniques.

The paper title information obtained from Google Scholar was tokenised to represent paper titles as a series of words of interest. This included the removal of common English language stop words (“and”, “the”, “of” etc.) based off a standard list for the library used. Footnote 1 In addition, the word “among” was removed. Two further minor changes were made to improve wording that was not picked up by the standard tokenisation process. This saw the token “student” replaced by “students” and the token “toward” replaced by “towards”. This decision was made to allow these common terms to be clustered together and improve the readability of the final results, rather than see two similar terms occupy two lots of results and confuse matters.

Table 4 shows the information collected and computed for each record in the data sets, along with an indicative example. As well as information collected directly from Google Scholar and subsequently cleansed, this includes information computed using standard NLP techniques of unigrams, bigrams and trigrams. A sentiment analysis score for each title is also calculated to determine if this represents positive, neutral or negative integrity. In the case of the example of Eshet et al. ( 2014 ) shown in Table 4 , the overall sentiment is considered to be negative, with the machine learning process likely to have made this judgement through the use of the terms “ traits ” and “ academic dishonesty ” in the paper title.

Research Methodology Limitations

Some natural limitations of the approach used within this investigation are worthy of mention. The data sets represent a snapshot of content on a live source of data, one that continually receives updates and corrections. The volume of citations observed in May 2020 will be different to that which would have been seen at the end of 2019, the cut-off point for including papers in the data sets. Even then, official publication dates can differ from the date papers were first available to be read and cited. This stems from the advent of papers being published online first before they are assigned to a journal issue.

The analysis presented focuses on paper titles, rather than paper abstracts or their contents. This assumes that titles accurately reflect the contents of the papers. There will be exceptions to this, for instance when a title is written for shock value or to encourage readership, in much the same way that newspaper headlines can be written to draw attention. The tendency for authors to think about search engine optimisation when organising papers is also a relatively recent change that may have influenced the choice of titles within the field.

Only a single source, Google Scholar, is used for data collection. The quality of the data sets is limited to the quality of the underlying source. The resulting data sets did require manual clean up and it is likely that a small number of errors remain. The time afforded for data cleansing and consistency checking was used strategically, focusing most on the secondary data sets since these are likely to have the greatest influence on future practice. Small errors in the primary data set (A) of 8,507 items should have no discernible effect on the accuracy of the overall results. These results are still of importance to the wider academic integrity research field.

Results and Discussion

Most frequently occuring unigrams, bigrams and trigrams in paper titles.

Table 5 summarises the 10 unigrams, bigrams and trigrams seen most frequently in the primary data set (A). Each of these measures provides insight into academic integrity research at different levels of granularity.

The unigram data indicates that 7,161 unique unigrams were observed in the primary data set, with a total of 60,402 occurrences. This shows a mean of 8.43 occurrences per unigram and a standard deviation of 477.63. The top 10 ranked unigrams covered 17,203 of those occurrences between then (28.48%). The unigram list does not appear to be particularly insightful.

The bigram data provides more useful level of granularity, with 30,169 unique bigrams and a total of 51,898 occurrences. That is a mean of 1.72 occurrences per bigram, with standard deviation of 11.22. The top 10 ranked bigrams cover 4,620 occurrences (8.90%). The first and second ranked bigrams “ academic integrity ” (seen in 1,234 occurrences, that is 2.37%) and “ academic dishonesty ” (seen in 1,068 occurrences or 2.06%) indicate the close relationship between the use of these two terms.

The trigram data indicates variety across paper titles, with 36,417 unique trigrams observed across 43,420 occurrences, giving a mean of 1.19 occurrences per bigram and a standard deviation of 1.42. Between them, the top 10 ranked trigrams cover only 524 occurrences (1.21%). The terms make intuitive sense and the quadgram “ source code plagiarism detection ” stands out as formable from the second and fifth ranked trigrams, indicating the interest in academic integrity techniques often considered most specific to Computer Science.

Exploration of Bigram Data

The bigram level provides the opportunity to further explore the primary and secondary data sets. Figure  4 shows the data obtained from the primary data set in more detail.

Top 25 bigrams observed in primary data set

Categorising the bigrams provides an indication of the topics of most interest to academic integrity researchers. The positive integrity terms “ academic integrity ”, “ academic honesty ” and “ integrity education ” can be combined to cover 1,568 out of 51,898 occurrences (3.02%). Accordingly, the negative integrity terms “ academic dishonesty ”, “ academic misconduct ” and “ student cheating ” can be combined to give 1,792 out of 51.898 (3.45%) occurrences, suggesting a slight bias towards negativity in paper titles. Other terms suggest wider areas of interest, including the academic level of students (such as university, college and high school), academic integrity challenges (such as plagiarism and contract cheating), methods of addressing challenges (such as through case studies and plagiarism detection), issues of interest to particular research sub groups (academic writing, source code plagiarism and plagiarism detection), as well as the type of data hoped to be gathered in many research projects (perceptions and attitudes).

The data sets were further interrogated to identify how many of the 25 most frequent bigrams occurred in the paper titles, as well as the number of the three positive bigrams (“ academic integrity ”, “ academic honesty ” and “ integrity education ”) and three negative bigrams (“ academic dishonesty ”, “ academic misconduct ” and “ student cheating ”) seen in those titles. Particular attention is paid to the most prolific of those terms, “ academic integrity ” and “ academic dishonesty ” and these are also analysed separately. The results are seen in Tables 6 and 7 .

Table 6 suggests that when more of the most frequently occurring bigrams are included in paper titles, those papers are more likely to be cited. They also suggest that the interest in negative integrity is greater than that in positive integrity. For example, in the 1000 most cited papers data set (D), the average paper title contains 0.113 out of the 3 positive bigrams, but contains 0.240 out of the 3 negative bigrams, an increase of 112.39%. A similar finding can be observed when comparing the use of the bigram “academic integrity” with “academic dishonesty”. The negative bigram is most frequent in all three of the most cited data sets (B, C and D).

There is one clear exception to this finding and that comes from the most prolific authors data set (E). This group uses 0.330 out of 3 positive bigrams per paper title, accompanied by only 0.205 out of 3 negative bigrams, showing that the titles they use contain 60.98% more positive than negative bigrams. Similarly, the prolific authors use the bigram “academic integrity” in almost one third of their paper titles, 136.76% more of the time than they use “academic misconduct”.

Further analysis show variety in number of the 25 most frequent bigrams included in paper titles. This is summarised in Table 8 .

From the primary data set (A), 43.52% of paper titles do not contain any of the 25 most frequent bigrams. For the most prolific authors data set (E), that percentage is only 23.48%, suggested that the researchers writing regularly in this field are familiar with the wider literature, the research base and the terminology to use. In all five data sets, the modal number of the most frequent bigrams used in a paper title is 1. There are 9 cases out of 8,507 records (0.11%) where four bigrams are used, sometimes as part of overlapping bigrams.

Table 9 compares the use of the bigrams “academic integrity” and “academic dishonesty” in paper titles. This indicates a perhaps alarming result, that papers in the field are more likely to be highly cited if they take a negative integrity stance. Once again, the most prolific authors buck this trend. From data set B, none of the 10 most academic integrity papers of all time contain “academic integrity” in their title, or indeed any of the positive keywords that have been identified from the 25 most frequently used bigrams.

Sentiment Analysis

The paper titles in the data sets were analysed using sentiment analysis techniques to determine if they represented positive, neutral or negative sentiment. A summary of the percentage of paper titles falling within each of these sentiments is shown in Table 10 .

For data sets A to D, the modal sentiment is neutral, although the most interesting comparisons lie between positive and negative sentiment. The results from the primary data set (A) show that titles are slightly more likely to be viewed as having positive sentiment rather than negative sentiment, but this is not consistently the case across all the data sets. In particular, the most cited data sets (B, C and D) show more negative than positive sentiment.

Different results are seen from the most prolific authors data set (E), where 120 out of 264 paper titles (45.45%) are computed to have positive sentiment, making this the modal sentiment group. Since the negative sentiment group contains 60 paper titles, this represents a 100% increase.

Considering a null hypothesis that, if randomly and independently determined, one third of paper titles should each show positive, neutral and negative sentiment, Pearson’s chi-squared test shows statistical significance for data sets A, C and D at the 0.001% level and for data set C at the 5% level. Data set B does not show statistical significance, but strictly speaking the sample size is too small for Pearson’s test to be valid.

A further element of investigation aims to address how the sentiment of paper titles has developed over time. The results are shown in Fig.  5 .

Cumulative Sentiment Analysis of Paper Titles

Figure  5 provides a cumulative plot of the percentage of paper titles that were computed to have positive, neutral and negative sentiment. That is, Fig.  5 shows the sentiment results from all the papers published up to a given point in time. This trend shows promise if a move towards positive integrity is considered desirable. Although historically the sentiment of paper titles has been strongly negative, neutral sentiment overtook negative sentiment for the first time in 2003. Positive sentiment then overtook negative sentiment in 2008. The current trend shows a continued decline in the use of negative sentiment.

The answer to one final question may interest researchers in this field. Does having positive sentiment in a paper title title affect the number of citations that paper is likely to obtain? Across the primary data set (A) as a whole, papers received an average of 14.27 citations. The paper titles with positive sentiment received 10.60 citations. The papers with neutral sentiment received 15.39 citations. The papers with negative sentiment received 17.46 citations. It would appear that developing paper titles with negative sentiment affords a good way to get work cited within the academic integrity research field.

Conclusion and Recommendations

This paper represents the first study of its kind in the academic integrity research field, using the largest known data set of academic integrity research publications as its base. The analysis shows that academic integrity research is a field with rapid growth, but citations have been built upon publications with a negative concept of integrity. Both bigram analysis and sentiment analysis show a similar view of negative integrity, but with pockets of positive integrity shining through.

Many opportunities exist to take this research forward. Similar techniques can be applied to other fields, or to specialised subjects within the academic integrity area. The bigram technique has shown the existence of many long-tail keywords that are suitable for literature reviews and more detailed analysis. The sentiment analysis approach used is not specific to academic integrity and could be further optimised through the development of training data sets. In addition, it would be interesting to apply these techniques to paper abstracts and full papers to see if the same results hold. Academic integrity researchers and practitioners may find it useful to develop more NLP and linguistical analysis skills. Many of the techniques applied to research are already akin to those which can be applied to forensic investigation of student work to detect plagiarism and contract cheating (Ison, 2020 ; Johnson & Davies, 2020 ).

Although not an intended focus of the investigation, the data serendipitously revealed that there appears to be a question to be posed regarding the value of much academic integrity research. In the primary data set developed for this paper, 2854 out of 8507 papers (33.55%) have never received a single citation. In addition, threats to research integrity were observed when examining the data set. A 2019 paper was found published in three different journals by the same suspect publisher with only slight changes to the paper title and abstract. Paper citation cartels seem to be developing, with single papers having a large group of authors, each of whom then go on to cite as many papers as possible by members of the group. The effect seems to be an artificial bump up the citation metrics for all members. In a field like academic integrity, researchers also need to hold their own practices up to the highest standards.

There are issues that need to be addressed regarding what content should be placed in research repositories and how Google Scholar results are produced. Students can be referred to Google Scholar as a valid starting point for their own research, but not all search results are suitable for this purpose. One university repository contains an archive of blog posts by researchers, but these are now listed by Google Scholar as if they are academic papers. There are also many examples of contract cheating providers finding ways to have their content added to Google Scholar, complete with visible adverts. The promotional methods of the contract cheating industry have already been observed as being highly suspect (Lancaster, 2019 ) and this is providing yet another method through which students can be brought into their marketing funnel.

One of the biggest disputes in the academic integrity community surfaced continually throughout this paper. Is the best terminology to use in research “academic integrity” or “academic dishonesty”? Should researchers take the opportunity to introduce a more positive viewpoint of the field? There is much historical interest to the use of the term “academic dishonesty” but this term may no longer be necessary. Despite this, research papers that take a negative view of integrity, using terms such as cheating, dishonesty and misconduct, do drive an emotional response in a manner that integrity does not seem to do. Such papers then benefit from more citations and drive future research. It is something of a vicious circle.

This paper has demonstrated that it is possible to present the academic integrity research field using positive terminology. Several of the most prolific authors in the field are doing just that. More publications appear to be taking a positive integrity view than ever before. Emerging academic integrity researchers can and should be encouraged to model their approach on such papers and researchers. But further effort needs to be made by the academic integrity community to promote such papers and to show that research into positive integrity is possible, worthwhile and of value.

Perhaps then a move to purely talk about positive integrity is a step too far. As the sentiment analysis presented in this paper has demonstrated, the most recent trend in paper title construction has been a move towards titles devoid of positive or negative intention. Researchers in related fields talk about ethical neutrality. At the start of this paper, a quote from Barnes ( 1904 ) talked about social responsibility. Too often, academic integrity researchers are the same people who are the practitioners working on academic integrity in the classroom, teaching students and often awarding penalties for academic integrity breaches. Due to the nature of the research field, true independence of research from practice and teaching may be impossible. Aiming instead for neutrality as to how research in the academic integrity field is presented may then provide the best future solution for all concerned.

The default stop words provided within the library used are: i, me, my, myself, we, our, ours, ourselves, you, you're, you've, you'll, you'd, your, yours, yourself, yourselves, he, him, his, himself, she, he', her, hers, herself, it, t', its, itself, they, them, their, theirs, themselves, what, which, who, whom, this, that, hat'l, these, those, am, is, are, was, were, be, been, being, have, has, had, having, do, does, did, doing, a, an, the, and, but, if, or, because, as, until, while, of, at, by, for, with, about, against, between, into, through, during, before, after, above, below, to, from, up, down, in, out, on, off, over, under, again, further, then, once, here, there, when, where, why, how, all, any, both, each, few, more, most, other, some, such, no, nor, not, only, own, same, so, than, too, very, s, t, can, will, just, don, on', should, should've, now, d, ll, m, o, re, ve, y, ain, aren, aren't, couldn, couldn't, didn, didn't, doesn, doesn't, hadn, hadn't, hasn, hasn't, haven, haven't, isn, isn't, ma, mightn, mightn't, mustn, mustn't, needn, needn't, shan, shan't, shouldn, shouldn't, wasn, wasn't, weren, weren't, won, won't, wouldn, wouldn't.

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Lancaster, T. Academic Dishonesty or Academic Integrity? Using Natural Language Processing (NLP) Techniques to Investigate Positive Integrity in Academic Integrity Research. J Acad Ethics 19 , 363–383 (2021). https://doi.org/10.1007/s10805-021-09422-4

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Literature Review: Academic Dishonesty – What Causes It, How to Prevent It

by Thomas Keith | Nov 16, 2018 | Instructional design

Note:  For further information on academic dishonesty and academic integrity, please see our series Combating Academic Dishonesty . Part 1 | Part 2 | Part 3

Academic dishonesty, which encompasses behaviors such as cheating, plagiarism, and falsification of data or citations, is a widespread and troubling phenomenon in higher education.  (For the full spectrum of behaviors that qualify as academic dishonesty, see Berkeley City College’s What Is Academic Dishonesty? )  It may be as simple as looking over a classmate’s shoulder during a quiz or as elaborate as hiring a ghostwriter online for a course paper, but whatever the method employed, academic dishonesty harms the learning experience and gives cheaters an unfair advantage over those who abide by the rules.  This post examines some of the chief factors that lead to academic dishonesty among college students, as determined by empirical research in the field, and offers suggestions to faculty and instructors on ways to reduce the likelihood of dishonest conduct among their students.

What Causes Academic Dishonesty?

There is no single explanation for the occurrence of dishonest behavior in college.  Studies suggest that most students realize academic dishonesty is morally wrong, but various outside factors or pressures may serve as “neutralizers,” allowing students to suppress their feelings of guilt and justify their dishonest acts to themselves (Baird 1980; Haines et al. 1986; Hughes and McCabe 2006).  In certain cases, dishonest behavior may arise not from willful disregard for the rules of academic integrity, but from ignorance of what those rules are.  Some common reasons for students’ engaging in academic dishonesty are given below.

Poor time management

Particularly in their early years of college, many students have difficulties with managing their time successfully.  Faced with demands on their out-of-class time from athletics, extracurricular clubs, fraternities and sororities, etc., they may put off studying or working on assignments until it is too late for them to do a satisfactory job.  Cheating then appears attractive as a way to avoid failure (Haines et al. 1986).

Academic pressures

Sometimes a student must maintain a certain GPA in order to receive merit-based financial aid, to participate in athletics, or even to continue receiving financial support from his/her family. Even high-achieving students may turn to academic dishonesty as a way to achieve their target GPA.  Academic pressures can be worsened in courses that are graded on a curve: with the knowledge that only a fixed number of As can be awarded, students may turn to dishonest methods of surpassing their classmates (Whitley 1998; Carnegie Mellon University ).

In very large classes, students may feel anonymous; if the bulk of their interaction is with teaching assistants, they may regard the instructor as distant and unconcerned with their performance.  This can increase the temptation to cheat, as students rationalize their dishonest behavior by assuming that the instructor “doesn’t care” what they do.  Not surprisingly, this can often be a danger in online courses, since course sizes can be huge and students do not normally interact with their instructors face-to-face ( Carnegie Mellon University ).

Failure to understand academic conventions

The “rules” of academic writing often appear puzzling to students, particularly those who have not had extensive practice with academic writing in high school.  The Internet has arguably exacerbated this problem; the easy availability of information (accurate or otherwise) on websites has led many students to assume that all information sources are de facto public property and need not be cited, which leads to unintentional plagiarism.  Faculty and instructors should not take for granted that their students simply “know” when they must cite sources and how they should do so (Perry 2008).  In addition, the ready availability of websites on every topic imaginable has had a deleterious effect on students’ ability to assess sources critically.  Some students simply rely upon whichever site comes up at the top of a Google search, without considering the accuracy or potential biases of the information with which they are being presented.

Cultural factors

Related to the above, international students may face particular challenges in mastering the conventions of academic writing.  They do not necessarily share Western/American understandings of what constitutes “originality,” intellectual property rights, and so forth, and it often takes time and practice for them to internalize the “rules” fully, especially if English is not their first language.  In addition, students who come from cultures where collaborative work is common may not realize that certain assignments require them to work entirely on their own (Currie 1998; Pecorari 2003; Hughes and McCabe 2006; Abasi and Graves 2008).

The academic pressures common to all college students can be particularly acute for international students.  In some cultures (e.g. those of East Asia) excellent academic performance at the university level is vital for securing good jobs after graduation, and students may therefore believe that their futures depend upon receiving the highest possible grades.  When a student’s family is making sacrifices to send him/her overseas for college, s/he may be concerned about “letting the family down” by doing poorly in school, which can make academic dishonesty all the more tempting.

Low-Stakes Assignments

While some people may think of cheating as a risk only on high-stakes assignments (course papers, final exams, and the like), it can easily occur on low-stakes assignments as well.  In fact, the very lack of grade weight that such assignments bear can encourage dishonesty: students may conclude that since an assignment has little or no bearing on their course grade, it “doesn’t matter” whether or not they approach it honestly.  For this reason, it is vital to stress to students the importance of honest conduct on all assignments, whether big or small.  The University does not take grade weight into account when deciding whether academic dishonesty has occurred; plagiarism is plagiarism and cheating is cheating, even if the assignment in question is worth zero points.

Technology and Academic Dishonesty

The rapidly increasing sophistication of digital technology has opened up new avenues for students bent on academic dishonesty.  Beyond simply cutting-and-pasting from webpages, an entire Internet economy has sprung up that offers essays for students to purchase and pass off as their own.  Students may also use wireless technology such as Bluetooth to share answers during exams, take pictures of exams with their smartphones, and the like (McMurtry 2001; Jones, Reid, and Bartlett 2008; Curran, Middleton, and Doherty 2011).  Research suggests that the use of technology creates a “distancing” effect that makes students’ guilt about cheating less acute ( Vanderbilt University ).

How Can Faculty and Instructors Combat Academic Dishonesty?

There is no panacea to prevent all forms of dishonest behavior.  That said, at each step of the learning design process, there are steps that faculty and instructors can take to help reduce the likelihood of academic dishonesty, whether by making it more difficult or by giving students added incentive to do their work honestly.

Course Management and Syllabus Design

The sooner students are informed about the standards of conduct they should adhere to, the greater the likelihood that they will internalize those standards (Perry 2010).  This is why it is worthwhile for faculty to devote a portion of their syllabus to setting standards for academic integrity.  Consider setting the tone for your course by offering a clear definition of what constitutes academic dishonesty, the procedure you will follow if you suspect that dishonest behavior has occurred, and the penalties culprits may face.  Include a link to UChicago’s statement on Academic Honesty and Plagiarism .  If you have a Canvas course site, you can create an introductory module where students must read a page containing your academic integrity policies and “mark as done,” or take a quiz on your policies and score 100%, in order to receive credit for completing the module.

If your syllabus includes many collaborative assignments, it can also be useful to explain clearly for which assignments collaboration is permitted and which must be done individually.  You can also specify what you consider acceptable vs. unacceptable forms of collaboration (e.g. sharing ideas while brainstorming is allowed, but copying one another’s exact words is not).

Finally, consider including information in your syllabus about resources available to students who are having academic difficulties, such as office hours and tutoring.  Students who are facing difficulties with time management, executive function, and similar issues may benefit from the Student Counseling Service’s Academic Skills Assessment Program (ASAP) .   The University’s Writing Center  offers help with mastering academic writing and its conventions.  Encourage your students to avail themselves of these resources as soon as they encounter difficulties.  If they get help early on, they will be less likely to feel desperate later and resort to dishonest behavior to raise their grade (Whitley 1998).

In general, making your expectations clear at the outset of your course helps to build a strong relationship between you and your students.  Your students will feel more comfortable coming to you for help, and they will also understand the risks they would be running if they behaved dishonestly in your course, which can be a powerful deterrent.

Assignment Design

When crafting assignments such as essays and course papers, strive for two factors: originality and specificity.  The more original the topic you choose, and the more specific your instructions, the less likely it is that students will be able to find a pre-written paper on the Internet that fits all the requirements (McMurtry 2001).  Changing paper topics from year to year also avoids the danger that students may pass off papers from previous years as their own work.  You might consider using a rubric with a detailed breakdown of the factors you will be assessing in grading the assignment; Canvas offers built-in rubric functionality .

If an assignment makes up a large percentage of your students’ final grade (e.g. a course paper), you might consider using “scaffolding”.  Have the students work up to the final submission through smaller, lower-stakes sub-assignments, such as successive drafts or mini-papers.  This has the double benefit of making it harder for students to cheat (since you will have seen their writing process) and reducing their incentive to cheat (since their grade will not be solely dependent upon the final submission) ( Carnegie Mellon University ).

In the case of in-class exams, you may find it worthwhile to create multiple versions of an exam, each with a separate answer key.  Even as simple an expedient as placing the questions in a different order in different versions makes it harder for students to copy off one another’s work or share answer keys ( Carnegie Mellon University ).

Technological Tools to Prevent Academic Dishonesty

Even as students have discovered more sophisticated ways to cheat, educational professionals and software developers have created new technologies to thwart would-be cheaters.  Canvas, the University’s learning management system, includes several features intended to make cheating more difficult.

By default, the Files tab in Canvas is turned off when a new course is created.  This prevents students from accessing your course files and viewing files they should not, such as answer keys or upcoming exam questions.  If you choose to enable Files in your course, you should place all sensitive files in locked or unpublished folders to render them invisible to students.  For more details, see this post .

If you are using Canvas Quizzes in your course, you can choose from a number of options that increase the variation between individual students’ Quizzes and thus decrease the chances of cheating.  These including randomizing answers for multiple-choice questions; drawing randomly selected questions from question groups; and setting up variables in mathematical questions, so that different students will see different numerical values.  For more details, see this post .

Several different computer programs have been developed that claim to detect plagiarism in student papers, usually by comparing student submissions against the Internet, a database of past work, or both, and then identifying words and phrases that match. Viper follows a “freemium” model, while the best-known subscription-based plagiarism checker, Turnitin , is currently licensed only by the Law School at the University of Chicago.  These programs can be helpful, but bear in mind that no automatic plagiarism checker is 100% accurate; you will still need to review student work yourself to see whether an apparent match flagged by the software is genuine plagiarism or not (Jones, Reid, and Bartlett 2008).  Also be aware that Turnitin and some other plagiarism checkers assert ownership rights over student work submitted to them, which can raise issues of intellectual property rights.

In addition to detecting plagiarism after the fact, there are technological tools that can help prevent it from occurring in the first place.  Citation managers such as Endnote and Zotero are excellent ways to help students manage their research sources and cite them properly, especially when writing longer papers that draw on a wide range of source material.  The University of Chicago Library offers a detailed guide to citation managers , along with regular workshops on how to use them .

What to Do if You Suspect Academic Dishonesty

If you suspect that academic dishonesty may have occurred in one of your courses, the University has resources to which you can turn.  For undergraduates, it is best to begin by speaking to the student’s academic adviser .  You can find out which adviser is assigned to a student in your course by visiting Faculty Access and looking at the “Advisor” column in the course roster.  If you have questions about disciplinary procedures specific to the College, you can contact the Office of College Community Standards, headed by Assistant Dean of Students Stephen Scott .   For graduate students, the appropriate area Dean of Students can provide information about the correct disciplinary procedures to follow.

The fight against academic dishonesty is a difficult one, and will continue to be so for the foreseeable future.  But if faculty and instructors give careful thought to the causes of student misconduct and plan their instructional strategies accordingly, they can do much to curb dishonest behavior and ensure that integrity prevails in the classroom.

Bibliography

Journal articles.

• Abasi, Ali R., and Barbara Graves.  “Academic Literacy and Plagiarism: Conversations with International Graduate Students and Disciplinary Professors.”   Journal of English for Academic Purposes 7.4 (Oct. 2008), 221-233.  
• Baird, John S., Jr.  “Current Trends in College Cheating.”   Psychology in the Schools 17 (1980), 515-522.  
• Curran, Kevin, Gary Middleton, and Ciaran Doherty.  “Cheating in Exams with Technology.” International Journal of Cyber Ethics in Education 1.2 (Apr.-Jun. 2011), 54-62.  
• Currie, Pat.  “Staying Out of Trouble: Apparent Plagiarism and Academic Survival.”   Journal of Second Language Writing 7.1 (Jan. 1998), 1-18.  
• Haines, Valerie J., et al.  “College Cheating: Immaturity, Lack of Commitment, and the Neutralizing Attitude.”   Research in Higher Education 25.4 (Dec. 1986), 342-354.  
• Hughes, Julia M. Christensen, and Donald L. McCabe.  “Understanding Academic Misconduct.” Canadian Journal of Higher Education 36.1 (2006), 49-63.  
• Jones, Karl O., Juliet Reid, and Rebecca Bartlett. “Cyber Cheating in an Information Technology Age.” In R. Comas and J. Sureda (coords.). “Academic Cyberplagiarism” [online dossier]. Digithum: The Humanities in the Digital Era 10 (2008), n.p. UOC. [Accessed: 26/09/18] ISSN 1575-2275. 
• McMurtry, Kim.  “E-Cheating: Combating a 21st Century Challenge.”   Technological Horizons in Education Journal 29.4 (Nov. 2001), 36-40.
• Pecorari, Diane.  “Good and original: Plagiarism and patchwriting in academic second-language writing.”   Journal of Second Language Writing 12.4 (Dec. 2003), 317-345.
• Perry, Bob.  “Exploring Academic Misconduct: Some Insights into Student Behaviour.”   Active Learning in Higher Education 11.2 (2010), 97-108.  
• Whitley, Bernard E.  “Factors Associated with Cheating among College Students: A Review.”   Research in Higher Education 39.3 (Jun. 1998), 235-274.  

Web Resources

• Berkeley City College:  http://www.berkeleycitycollege.edu/wp/de/what-is-academic-dishonesty/
• Carnegie Mellon University: https://www.cmu.edu/teaching/solveproblem/strat-cheating/index.html
• University of Chicago: https://college.uchicago.edu/advising/academic-honesty |  https://studentmanual.uchicago.edu/Policies
• Colorado State University: https://tilt.colostate.edu/integrity/resourcesFaculty/whyDoStudents.cfm
• Harvard University (Zachary Goldman): https://www.gse.harvard.edu/uk/blog/youth-perspective
• Oakland University: https://www.oakland.edu/Assets/upload/docs/OUWC/Presentations%26Workshops/dont_fail_your_courses.pdf
• Vanderbilt University (Derek Bruff): https://cft.vanderbilt.edu/2011/02/why-do-students-cheat/

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Exploring Academic Integrity in Your Research: A Tutorial

• 2 - Scholarly Conversation
• 3 - Scholarly Conversation & Justice
• 4 - LACE at the University of Oregon
• 5 - Academic Discourse
• 6 - Student Responsibilities
• 7 - Academic Discourse & Student Success

2 - Academic Honesty

• 3 - Academic Honesty
• 4 - Academic Dishonesty
• 5 - Academic Dishonesty
• 6 - Academic Dishonesty
• 7 - Academic Dishonesty
• 8 - Academic Dishonesty
• 9 - Academic Dishonesty
• 10 - Academic Dishonesty
• 11 - Plagiarism
• 12 - Plagiarism
• 13 - Paraphrasing
• 14 - Paraphrasing
• 15 - Consequences
• 16 - Consequences
• 17 - Review
• 2 - Attribution
• 3 - Citations
• 4 - Citations
• 5 - Plagiarism
• 6 - Plagiarism
• 7 - To Cite or Not?
• 8 - To Cite or Not?
• 9 - To Cite or Not?
• 10 - To Cite or Not?
• 11 - To Cite or Not?
• 12 - To Cite or Not?
• 13 - Citation Styles
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• 11 - Review
• More Resources

What is academic honesty?

Academic honesty ensures acknowledgement of other people’s hard work and thought.  The International Center for Academic Integrity defines it as “a commitment, even in the face of adversity, to six fundamental values: honesty, trust, fairness, respect, responsibility, and courage. From these values flow principles of behavior that enable academic communities to translate ideals to action.”

Different cultures and traditions often have distinct definitions of what behaviors constitute academic honesty. For example, in some cultures, it is considered a sign of respect to use the exact wording of a well-known thinker, and attribution is considered unnecessary. However, that is not an accepted practice for scholars in the United States.

To learn more about cultural differences with regards to academic honesty, check out this book: Howard, Rebecca Moore. Standing in the Shadow of Giants : Plagiarists, Authors, Collaborators . Stamford, Conn.: Ablex Pub., c1999.

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Academic Integrity vs. Academic Dishonesty

Published on March 10, 2022 by Tegan George and Jack Caulfield. Revised on April 13, 2023.

Academic integrity  is the value of being honest, ethical, and thorough in your academic work. It allows readers to trust that you aren’t misrepresenting your findings or taking credit for the work of others.

Academic dishonesty (or academic misconduct) refers to actions that undermine academic integrity. It typically refers to some form of plagiarism , ranging from serious offenses like purchasing a pre-written essay to milder ones like accidental citation errors. Most of which are easy to detect with a plagiarism checker .

These concepts are also essential in the world of professional academic research and publishing. In this context, accusations of misconduct can have serious legal and reputational consequences.

Table of contents

Types of academic dishonesty, why does academic integrity matter, examples of academic dishonesty, frequently asked questions about plagiarism.

While plagiarism is the main offense you’ll hear about, academic dishonesty comes in many forms that vary extensively in severity, from faking an illness to buying an essay.

Prevent plagiarism. Run a free check.

Most students are clear that academic integrity is important, but dishonesty is still common.

There are various reasons you might be tempted to resort to academic dishonesty: pressure to achieve, time management struggles, or difficulty with a course. But academic dishonesty hurts you, your peers, and the learning process. It’s:

• Unfair to the plagiarized author
• Unfair to other students who did not cheat
• Damaging to your own learning
• Harmful if published research contains misleading information
• Dangerous if you don’t properly learn the fundamentals in some contexts (e.g., lab work)

The consequences depend on the severity of the offense and your institution’s policies. They can range from a warning for a first offense to a failing grade in a course to expulsion from your university.

• Faking illness to skip a class
• Asking for a classmate’s notes from a special review session held by your professor that you did not attend
• Crowdsourcing or collaborating with others on a homework assignment
• Citing a source you didn’t actually read in a paper
• Cheating on a pop quiz
• Peeking at your notes on a take-home exam that was supposed to be closed-book
• Resubmitting a paper that you had already submitted for a different course (self-plagiarism)
• Forging a doctor’s note to get an extension on an assignment
• Fabricating experimental results or data to prove your hypothesis in a lab environment
• Buying a pre-written essay online or answers to a test
• Falsifying a family emergency to get out of taking a final exam
• Taking a test for a friend

Academic integrity means being honest, ethical, and thorough in your academic work. To maintain academic integrity, you should avoid misleading your readers about any part of your research and refrain from offenses like plagiarism and contract cheating, which are examples of academic misconduct.

Academic dishonesty refers to deceitful or misleading behavior in an academic setting. Academic dishonesty can occur intentionally or unintentionally, and varies in severity.

It can encompass paying for a pre-written essay, cheating on an exam, or committing plagiarism . It can also include helping others cheat, copying a friend’s homework answers, or even pretending to be sick to miss an exam.

Academic dishonesty doesn’t just occur in a classroom setting, but also in research and other academic-adjacent fields.

Consequences of academic dishonesty depend on the severity of the offense and your institution’s policy. They can range from a warning for a first offense to a failing grade in a course to expulsion from your university.

For those in certain fields, such as nursing, engineering, or lab sciences, not learning fundamentals properly can directly impact the health and safety of others. For those working in academia or research, academic dishonesty impacts your professional reputation, leading others to doubt your future work.

Academic dishonesty can be intentional or unintentional, ranging from something as simple as claiming to have read something you didn’t to copying your neighbor’s answers on an exam.

You can commit academic dishonesty with the best of intentions, such as helping a friend cheat on a paper. Severe academic dishonesty can include buying a pre-written essay or the answers to a multiple-choice test, or falsifying a medical emergency to avoid taking a final exam.

The consequences of plagiarism vary depending on the type of plagiarism and the context in which it occurs. For example, submitting a whole paper by someone else will have the most severe consequences, while accidental citation errors are considered less serious.

If you’re a student, then you might fail the course, be suspended or expelled, or be obligated to attend a workshop on plagiarism. It depends on whether it’s your first offense or you’ve done it before.

As an academic or professional, plagiarizing seriously damages your reputation. You might also lose your research funding or your job, and you could even face legal consequences for copyright infringement.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. & Caulfield, J. (2023, April 13). Academic Integrity vs. Academic Dishonesty. Scribbr. Retrieved March 25, 2024, from https://www.scribbr.com/plagiarism/academic-dishonesty/

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Academic Honesty: Why It Matters in Psychology

In psychology, academic honesty is about so much more than getting in trouble..

Posted April 17, 2021 | Reviewed by Abigail Fagan

• All colleges and universities have academic honesty policies with serious consequences.
• Websites that pay to write student papers violate academic honesty and are becoming more abundant and aggressive.
• Academic honesty is inherently psychological, involving questions of curiosity, trust, morality, and future orientation.

The other day, while looking for a free plagiarism checker to use in addition to the one provided by my institution, I came across a website blatantly selling papers to students. This particular site promises, for a high fee per page, to write students completely unique papers that won’t get caught as plagiarism. They’ll even write your Ph.D. dissertation for you (uh…good luck defending that).

All professors are familiar with these sites. The fact that students are paying others to produce work for them is not a secret, at all. Most of us have caught students doing this, or versions of it, and though it’s exhausting and demoralizing, we’ve learned to deal with it semester after semester.

What is academic honesty?

This behavior falls under the heading of “academic honesty.” All colleges and universities have academic honesty policies that address issues like plagiarism and cheating, including serious consequences for violating them. I, for one, am particularly adept at detecting copy/paste/change-a-few-words plagiarism. Frankly, half the time it’s obvious because it’s incomprehensible. As many professors will commiserate, if I wasn’t so good at detecting it, life would be much easier.

Most of us on the policy enforcement side can relate stories with versions of, “But I bought the paper! I didn’t plagiarize, the person who wrote it did! I shouldn’t be held responsible!” In fact, I receive more and more pushback like that every semester: “My cousin wrote the paper for me and I had no idea she plagiarized! She should get in trouble, not me!”

Where does academic dishonesty come from?

We certainly understand that issues like plagiarism may come from lack of confidence in one’s writing skills, being unprepared for college, pressure, inaccessible resources, and the like, but overall, I’ve found it to be a matter of buy-in. Either students buy in to the concept of academic honesty or they don’t, and this has implications beyond school.

How is academic honesty linked to psychology?

I’m less concerned with magically convincing students to follow academic honesty policies than I am in getting them to think about why it is important in the context of psychology. Though I am indeed a prevention practitioner, I’m not naïve enough to think I can change someone’s mind about the value of academic honesty. I am, however, hopeful that those studying psychology will consider the following connections (and then some):

• Learning – You’re not learning much if you’re not doing the work. I once listened to an NPR story about students purchasing papers in which a student said, “I feel like I am doing my own work because I’m using my own money.” Come on. Psychology is all about learning. It’s a topic in every introductory psychology course. It’s usually an entire chapter in introductory psychology textbooks. We have classes specifically focused on it. One of the foundations of learning is that the learner be…involved.
• Morality – “What is moral?” students ask. I can’t answer that, but I am pretty confident that cheating is not. Again, this is a topic that is usually covered in introductory psychology and then over and over again in developmental psychology, social psychology, and more. You’ll even find “moral psychology” as its own field. Psychologist Lawrence Kholberg asked if subjects would steal a drug. Today, he could ask if you’d buy a term paper.
• Future orientation – Personality psychology research suggests that those with a “future orientation” tend to have better outcomes than those with a “present orientation.” The idea is that if you have a future orientation, you tend to, well, look to the future and anticipate future outcomes more than those who are focused solely on the present. While a concern with consequences is associated with mortality (e.g. Kholberg’s theory), the ability or tendency to envision potential consequences is associated with a future orientation. Could there be a more psychological question than, “Is it worth it?”
• Conscientiousness and trust – Conscientiousness is a core personality trait. Trust is essential in development and relationships. Academic dishonesty violates trust and displays low conscientiousness.
• Human services – Students often take psychology because it’s required for medical careers, careers involving working with children, and other human service careers. Go back to the first point about learning. I once had a nurse who tried to inject Heparin directly into my muscle. I had to fight to get her to inject it subcutaneously, as directed. When you work in a hospital, on a general surgery floor, not knowing where to safely inject a blood thinner is alarming. When you don’t do your own work, you don’t have a chance to learn and for a discipline preparing students to work with humans, especially children, everything associated with academic honesty, all of the above, is essential.
• Personal fable – Simply put, this component of David Elkind’s adolescent egocentrism theory suggests that adolescents tend to think they are special and unique. “It might happen to you, but it won’t happen to me.” I can’t tell you how many students are shocked and very angry when caught. In fact, I once read a Twitter thread from professors about the very real dangers associated with catching plagiarism. Many students are still in adolescence , and thinking you’re an exception who won’t get caught is a sure sign.
• Entitlement and violence – Speaking of anger, the idea that you’re special is linked to entitlement , a very psychological concept. In fact, those who study education research “academic entitlement,” in which students feel they should get a good grade just because they attended class or just because they turned in work. Having worked in domestic and sexual violence for a very long time, I know that entitlement is often coupled with violence, as challenges to one’s sense of entitlement frequently result in anger and aggression . Linking homework to violence seems incredible, but it’s a very real possibility.
• Behavioral consistency – As much as we may want to, professors generally can’t share information about other students with other professors. There’s no, “Hey, watch out for this student, they told me their cousin is doing all their homework for them.” However, all academic honesty policies do require some level of reporting to campus administration and they know about behavioral consistency, another psychological concept. This concept suggests that people tend to behave in a consistent manner; they behave in ways that match their past behavior. Need I say more?

One of the main reasons for academic honesty is scientific integrity. I didn’t address it above because, frankly, I find that’s not a very convincing argument, especially when these “pay for us to do your homework” sites target students so aggressively. I found a few more of these sites and recently used their online chat tool. Before I disclosed that I am a professor, and subsequently got kicked off, every single one guaranteed that my professor and my institution “wouldn’t find out.” That’s appalling, not just for the reasons above, but because we do find out, and it can ruin a student’s entire academic career .

Psychology is fascinating and fun. Why wouldn’t you want to learn it, anyway?

Ashley Maier teaches psychology at Los Angeles Valley College.

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Using Sources, Avoiding Plagiarism, and Academic Honesty

A key expectation of academic work is that what you submit is your own, and that you appropriately source words and ideas that are not your own. Since academic writing involves building on the ideas of others, knowing how to integrate that material with your own thinking is a fundamental skill for success. Writers who simply haven’t practiced that skill may find themselves submitting papers with unintentional plagiarism (which is by far the most common). The resources below explain what plagiarism is, and how to avoid it through careful use of source material, rhetoric, and citations. Please feel free to email us with any thoughts or suggestions!

What is Plagiarism?

Put simply, plagiarism is when you claim the words or ideas of others as your own. Since all work you submit during an academic program is presumed to be yours, even leaving out a citation can lead to unintentional plagiarism. Avoiding plagiarism means knowing how to integrate sources correctly into your writing, understanding the rules of the style guide you’re using, and having a big-picture understanding of academic honesty: the “why” behind all those seemingly arbitrary rules.

• Antioch University Plagiarism Policy

Integrating Sources

Any time you use someone else’s words or ideas (which you do in most academic papers), you need to be careful to track them through your research and drafting phases, attribute them in your writing phases, and ensure they are correctly cited during your final polishing phases. Integrating sources well starts with research–taking good notes, actively synthesizing as you read, and making sure you put other people’s words in quotes in your notes are all ways to avoid accidental plagiarism down the line. As you start to write, you’ll want to use quotations, paraphrases, and syntheses to describe other people’s ideas. Each integrates sources in a different way, and academic writers need to know how to do all three, and when each is appropriate. As you finish your paper, you need to able to include citations in a consistent and appropriate format so that readers of your work can locate the source you used for a given idea. In academic writing, it is expected that your work fits into an ongoing conversation; citing your sources helps your readers know who contributed before you, and how you used their ideas. Reading and Doing Research

• Active Reading Strategies
• Critical Reading Exercises
• Gathering Information
• Evaluating Research Generally
• Evaluating Empirical Research
• The Art of Integrating Sources
• Using Quotations
• A Short Guide to Paraphrasing

Style and Citations

Regardless of your field and specialty, you can rest assured that you will need to cite your sources and abide by the rules of a style guide. These resources focus on helping you manage those expectations, especially around the particulars of things like APA style.

• Citation Managers
• Antioch Seattle MA Psych Style Guidelines
• An Overview of APA Style
• Common Mistakes in APA Style

Other Resources:

• Visit the American Psychological Association website for updated information regarding APA style and formatting guidelines for writing in the psychology and social sciences.
• Visit the Modern Language Association website for updated information regarding MLA style and formatting guidelines for writing in the humanities.

  Academic Honesty

Part of academic writing is also managing your time and working sufficiently in advance to do your work well. If you are working at the last minute or find yourself committed, you may find yourself tempted to leave out a citation, to appropriate a quote, or even to copy and paste text from a source without attribution. While everyone understands the desperation that can lead to academic dishonesty, the choice to engage in intentional plagiarism is a serious breach of conduct with serious consequences. In an academic program, it can lead to your being put on academic probation or kicked out of the University. Beyond student writing, plagiarism can cause you to lose all credibility in your field and destroy your academic or professional career.

Healthy Approaches to Plagiarism: A Collaborative Response

Dorothy Capers,  AUS PsyD Student & Anne Maxham, Ph.D., Director of Writing Support   Plagiarism today goes beyond the flagrant taking of another’s piece of writing and turning it as your own. With the internet, facile copying and pasting of others’ words can wreak havoc on your academic integrity.

Caveat Scriptor!

(Writer Beware!)

Overview: Plagiarism is fundamentally the act of taking others’ words and using them as your own. The range of what identifies as plagiarism is complex: it may be intentional or unintentional; it may be in the form of paraphrases without citing the source, or word for word (seven or more words in sequence from the original source); or padding your writing with longer passages without citations. Being charged with “academic dishonesty” or “plagiarism” is a gut-wrenching experience that no student wants to risk. The impact of being questioned about your authenticity can result in losing confidence as a writer and even have you doubt your purpose in studying at the university. Beyond the emotional effects, other consequences can be dire, and sometimes result in failing the class, being put on academic probation, and worst of all expulsion from the university. All writers need to take precautions and make efforts to ensure that your writing is “all yours” and that you properly cite others’ words and ideas. One scenario of why it can happen to anyone: Many of us now compose directly on the computer and frequently have multiple documents opened at any given time. We “read” to find information to use in our writing. Frequently, we jump from online articles to our own document, copying and pasting material. At times, we’re writing papers with quick deadlines, and we might rush through this all-important step of first understanding the article content. Rather than fully “digesting texts,” we read for important information and key points to include in the paper. Our notes become lifted passages from texts rather than summarizing in our own words. We research and read for “context” rather than the “content”; that is, we read to finish our writing rather than fully understanding the topic or content. What you can do: To avoid unintentional plagiarism, stop long enough in your reading to think about what the author is saying. Put it in your own words. There’s an inherent danger in copying text and pasting into your own notes. And in doing so, writers can naively create a “fertile environment” for plagiarism to occur.  And it happens not just in academia. Take a look at what happened to well-known authors, and the consequences can ruin a career. Or musicians and the long lawsuits that follow. Remember, James Frey and the scandal after Oprah had selected his Million Little Pieces as one of her “reads”? Oprah felt betrayed and used. Her anger was palpable when she publicly lambasted him in her program: https://www.youtube.com/watch?v=ewC-KIe5qng http://www.csmonitor.com/Books/2011/1208/5-famous-plagiarism-and-fraud-accusations-in-the-book-world/Alex-Haley And recently, Neil Gorsuch was accused of plagiarizing parts of his book: http://www.politico.com/story/2017/04/gorsuch-writings-supreme-court-236891 So, we’ve developed this resource to help students take proactive measures to be academically honest. Before we move into the nitty gritty, we have some fundamentals:

• First, create a “working bibliography” of your resources. Put a number or a letter next to each and use that notation next to your quotes & paraphrases. That way, the sources for all quotes/paraphrases are identified.
• Cite all direct quotes, paraphrases, statistics, and unique ideas. Take the extra time to put quotation marks around words that are not yours. And don’t forget to post the page number of all direct quotes.
• direct quotes = citation
• paraphrases = citation
• statistics = citation
• unique concepts = citation
• when in doubt = citation
• If you’re not sure, you should seek writing support with your writing center or the VWC.

The Academic Conversation For those who want to write original work, learning how to enter the academic conversation is fundamental. While the academy is a place for active debate, most of us read materials given to us as passive “voyeurs” of a text. Of course, this is saying something about the implicit/explicit power dynamic between the faculty member and the student. Do we read to highlight what we think the faculty member wants us to read? Or do we read to wrestle with ideas? Frankly, given the reality that most of us read multiple texts each week, we’re lucky if we “digest” even one text.  The fact that most of us read – or submit a text— seldom questioning its content, style, or the intent of the author shows that we may be disempowered in the academic enterprise. Many students don’t realize that writing forces a reader to “digest” the material and to summarize as well as validate assertions by referring to the experts. So, active reading is essential in bringing the reader into the discourse. Since there are deep and multiple connections between reading and writing, we all need to learn and use strategies of active, critical reading (See the VWC Resources: “ Active Reading Strategies” and “ Critical Reading Exercises” )

If we think about academic reading and writing as a conversation, students have to carry the researchers forward in the conversation, even those with opposing views. Writing a paper is entering the conversation in an attempt to inform the reader of your unique learning through summarizing, paraphrasing, and citing other researchers. Ways to ensure Academic Authenticity: Validating that your writing is authentically yours and accurately reflecting your understanding of the topic begins early in your writing process.  Before writing, verify that you understand the assignment. Ask questions and request examples from the faculty member. Remember, what your instructors wants in an assignment is most important for your success. If you don’t understand, ask classmates and go to the writing center for additional support. Taking Notes: Take “real notes”: Don’t just lift full lines or passages from your reading. Be sure to write all notes in your own words, or put quotes around texts. If you’ve paraphrased, you still need to cite. So, put ( ) and the author, date, pg number. Defining the goals of your literature review will guide both your reading and your note-taking.   Peg Single Boyle, author of Demystifying Dissertation Writing (2009), offers a clear approach to “Citable Notetaking”:

• Pre-read your articles before taking notes
• Keep track of what’s summarized, paraphrased, or quoted.
• Choose  consistent formats for your notes. For example: If more than one article set up a spreadsheet to identify authors, article theme and quotes and paraphrases. This will help with putting your outline together when you start to write  (p 55-78).

The Virtual Writing Center has other resources available at the top of this page to help guide you to academic success. Tutorials: Want to see how much you know or don’t know about plagiarism? Spend a productive hour watching the tutorials and then take the “Certification Test” at the Indiana University resource: Tutorial: https://www.indiana.edu/~academy/firstPrinciples/tutorials/index.html Test: https://www.indiana.edu/~academy/firstPrinciples/certificationTests/index.html Finally: As a member of a discipline, you’re responsible to learn the style sheet of your field of practice (APA, Chicago, MLA, etc.).  Use online resources and manuals relevant to your field. If you’re unclear, seek help and work one-one with Mentor/VWC.  If you want professional help, go to the AU Writers’ Exchange (wex.antioch.edu).  Also review this handy checklist for APA Style that was designed for writers to refer to prior to submitting their papers. Writing support is designed to help students. With friendly student peer consultants, you may talk about your writing and get the support you need. You’re not alone.    References Boyle, P.S. (2009).  Demystifying dissertation writing. Stylus Pub: New York.

Resources for Faculty

• Responding to Plagiarism
• Plagiarism Checklist for Faculty

Academic Resources: Bronwyn T. Williams (2008). Trust, betrayal, and authorship: Plagiarism and how we perceive students.   Journal of Adolescent and and Adult Literacy 51 :4, 350 – 354. Abstract: Emotional responses to plagiarism are rarely addressed in professional literature that focuses on ethics and good teaching practices. Yet, the emotions that are unleashed by cases of plagiarism, or suspicions of plagiarism, influence how we perceive our students and how we approach teaching them. Such responses have been complicated by online plagiarism-detection services that emphasize surveillance and detection. My opposition to such plagiarism software services grows from the conviction that if we use them we are not only poisoning classroom relationships, but also we are missing important opportunities for teaching.

Howard, R., & Robillard, A. (2008). Pluralizing plagiarism : Identities, contexts, pedagogies . Portsmouth, NH: Boynton/Cook. Pluralizing Plagiarism offers multiple answers to this question — answers that insist on taking into account the rhetorical situations in which plagiarism occurs. While most scholarly publications on plagiarism mirror mass media’s attempts to reduce the issue to simple black-and-white statements, the contributors to Pluralizing Plagiarism recognize that it takes place not in universalized realms of good and bad, but in specific contexts in which students’ cultural backgrounds often play a role. Teachers concerned about plagiarism can best address the issue in the classroom — especially the first-year composition classroom — as part of writing pedagogy and not just as a matter for punishment and prohibition. . . “–Back cover.

Price, M. (2002). Beyond “Gotcha!”: Situating plagiarism in policy and pedagogy. College Composition and Communication, 54 (1), 88-115 Abstract:Plagiarism is difficult, if not impossible, to define. In this paper, I argue for a context-sensitive understanding of plagiarism by analyzing a set of written institutional policies and suggesting ways that they might be revised. In closing, I offer examples of classroom practices to help teach a concept of plagiarism as situated in context.

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National Research Council (US) and Institute of Medicine (US) Committee on Assessing Integrity in Research Environments. Integrity in Scientific Research: Creating an Environment That Promotes Responsible Conduct. Washington (DC): National Academies Press (US); 2002.

Integrity in Scientific Research: Creating an Environment That Promotes Responsible Conduct.

• Hardcopy Version at National Academies Press

2 Integrity in Research

The pursuit and dissemination of knowledge enjoy a place of distinction in American culture, and the public expects to reap considerable benefit from the creative and innovative contributions of scientists. As science becomes increasingly intertwined with major social, philosophical, economic, and political issues, scientists become more accountable to the larger society of which they are a part. As a consequence, it is more important than ever that individual scientists and their institutions periodically reassess the values and professional practices that guide their research as well as their efforts to perform their work with integrity.

Society's confidence in and support of research rest in large part on public trust in the integrities of individual researchers and their supporting institutions. The National Academies' report On Being a Scientist states: “The level of trust that has characterized science and its relationship with society has contributed to a period of unparalleled scientific productivity. But this trust will endure only if the scientific community devotes itself to exemplifying and transmitting the values associated with ethical scientific conduct” (NAS, 1995, preface). It is therefore incumbent on all scientists and scientific institutions to create and nurture a research environment that promotes high ethical standards, contributes to ongoing professional development, and preserves public confidence in the scientific enterprise (Grinnell, 1999; IOM, 2001; Resnik, 1998; Yarborough and Sharp, 2002).

Government oversight of scientific research is important, but such oversight, often in the form of administrative rules, typically stipulates what cannot be done; it rarely prescribes what should be done (see Chapter 4 for further discussion of the strengths and limitations of a regulatory approach). In essence, government rules define the floor of expected behavior. More, however, should be expected from scientists when it comes to the responsible conduct of research. By appealing to the conscience of individual scientists, the scientific community as a whole should seek to evoke the highest possible standard of research behavior. When institutions committed to promoting integrity in research support those standards, the likelihood of creating an environment that advances responsible research practices is greatly enhanced. It is essential that institutions foster a culture of integrity in which students and trainees, as well as senior researchers and administrators, have an understanding of and commitment to integrity in research.

The committee's task was to define integrity for the particular activity of research as conducted within contemporary society. Integrity has two general senses. The first sense concerns wholeness; the second, soundness of moral principle ( Oxford English Dictionary , 1989). Plato and subsequent philosophers have argued that leading the good life depends on a person's success in integrating moral, religious, and philosophical convictions. In conversations with experts in ethics and others, the committee found no consensus regarding whether a person could exhibit high integrity in research but not in other aspects of his life. Consequently, the committee decided to focus on the second aspect of integrity—namely, soundness of moral principle in the specific context of research practice.

• INTEGRITY IN RESEARCH

Integrity characterizes both individual researchers and the institutions in which they work. For individuals, it is an aspect of moral character and experience. 1 For institutions, it is a matter of creating an environment that promotes responsible conduct by embracing standards of excellence, trustworthiness, and lawfulness that inform institutional practices.

For the individual scientist, integrity embodies above all a commitment to intellectual honesty and personal responsibility for one's actions and to a range of practices that characterize responsible research conduct. These practices include:

• intellectual honesty in proposing, performing, and reporting research;
• accuracy in representing contributions to research proposals and reports;
• fairness in peer review;
• collegiality in scientific interactions, including communications and sharing of resources;
• transparency in conflicts of interest or potential conflicts of interest;
• protection of human subjects in the conduct of research;
• humane care of animals in the conduct of research; and
• adherence to the mutual responsibilities between investigators and their research teams.

Individual scientists work within complex organizational structures. (These structures and their interactions are described in detail in Chapter 3 .) Factors that promote responsible conduct can exert their influences at the level of the individual; at the level of the work group (e.g., the research group); and at the level of the research institution itself. These different organizational levels are interdependent in the conduct of research. Institutions seeking to create an environment that promotes responsible conduct by individual scientists and that fosters integrity must establish and continuously monitor structures, processes, policies, and procedures that:

• provide leadership in support of responsible conduct of research;
• encourage respect for everyone involved in the research enterprise;
• promote productive interactions between trainees and mentors;
• advocate adherence to the rules regarding all aspects of the conduct of research, especially research involving human subjects and animals;
• anticipate, reveal, and manage individual and institutional conflicts of interest;
• arrange timely and thorough inquiries and investigations of allegations of scientific misconduct and apply appropriate administrative sanctions;
• offer educational opportunities pertaining to integrity in the conduct of research; and
• monitor and evaluate the institutional environment supporting integrity in the conduct of research and use this knowledge for continuous quality improvement.

Leadership by individuals of high personal integrity helps to foster an environment in which scientists can openly discuss responsible research practices in the face of conflicting pressures. All those involved in the research enterprise should acknowledge that integrity is a key dimension of the essence of being a scientist and not a set of externally imposed regulatory constraints.

• INTEGRITY OF THE INDIVIDUAL SCIENTIST

As noted above, the committee has identified a range of key practices that pertain to the responsible conduct of research by individual scientists. The following sections elucidate the practices. 2

Intellectual Honesty in Proposing, Performing, and Reporting Research

Intellectual honesty in proposing, performing, and reporting research refers to honesty with respect to the meaning of one's research. It is expected that researchers present proposals and data honestly and communicate their best understanding of the work in writing and verbally. The descriptions of an individual's work found in such communications frequently present selected data from the work organized into frameworks that emphasize conceptual understanding rather than the chronology of the discovery process. Clear and accurate research records must underlie these descriptions, however. Researchers must be advocates for their research conclusions in the face of collegial skepticism and must acknowledge errors.

Accuracy in Representing Contributions to Research Proposals and Reports

Accuracy in representing one's contributions to research proposals and reports requires the assignment of credit. It is expected that researchers will not report the work of others as if it were their own. This is plagiarism. Furthermore, they should be honest with respect to the contributions of colleagues and collaborators. Decisions regarding authorship are best anticipated at the outset of projects rather than at their completion. In publications, it should be possible in principle to specify each author's contribution to the work. It also is expected that researchers honestly acknowledge the precedents on which their research is based.

Fairness in Peer Review

Fairness in peer review means that researchers should agree to be peer reviewers only when they can be impartial in their judgments and only when have revealed their conflicts of interest. Peer review functions to maintain the excellence of published scientific work and ensure a merit-based system of support for research. A delicate balance pervades the peer-review system, because the best reviewers are precisely those individuals who have the most to gain from “insider information”: they are doing similar work and they will be unable to “strike” from memory and thought what they learn through the review process. Investigators serving as peer reviewers should treat submitted manuscripts and grant applications fairly and confidentially and avoid using them inappropriately.

Collegiality in Scientific Interactions, Including Communications and Sharing of Resources

Collegiality in scientific interactions, including communications and sharing of resources requires that investigators report research findings to the scientific community in a full, open, and timely fashion. At the same time, it should be recognized that the scientific community is highly competitive. The investigator who first reports new and important findings gets credited with the discovery.

It is not obvious that rapid reporting is the approach that is always the most conducive to progress. Intellectual property provisions and secrecy allow for patents and licensure and encourage private investment in research. Furthermore, even for publicly funded research, a degree of discretion may permit a research group to move ahead more efficiently. Conversely, an investigator who delays reporting important new findings risks having others publish similar results first and receiving little recognition for the discovery. Knowing when and how much to tell will always remain a challenge in scientific communication.

Once scientific work is published, researchers are expected to share unique materials with other scientists in a reasonable fashion to facilitate confirmation of their results. (The committee recognizes that there are limits to sharing, especially when doing so requires a time or cost commitment that interferes with the function of the research group.) When materials are developed through public funding, the requirement for sharing is even greater. Public funding is based on the principle that the public good is advanced by science conducted in the interest of humanity. This commitment to the public good implies a responsibility to share materials with others to demonstrate reproducibility and to facilitate the replication and validation of one's work by responding constructively to inquiries from other scientists, particularly regarding methodologies.

Collegiality and sharing of resources is also an important aspect of the interaction between trainees and their graduate or postdoctoral advisers. Students and fellows will ultimately depart the research team, and discussion of and planning for departure should occur over the course of their education. Expectations about such issues as who inherits intellectual property rights to a project or to the project itself upon the trainee's departure should be discussed when the trainee first joins the research group and should be revisited periodically over the course of the project (NAS, 2000).

Transparency in Conflicts of Interest or Potential Conflicts of Interest

A conflict of interest in research exists when the individual has interests in the outcome of the research that may lead to a personal advantage and that might therefore, in actuality or appearance, compromise the integrity of the research. The most compelling example is competition between financial reward and the integrity of the research process. Religious, political, or social beliefs can also be undisclosed sources of research bias.

Many scientific advances that reach the public often involve extensive collaboration between academia and industry (Blumenthal et al., 1996; Campbell et al., 1998; Cho et al., 2000). Such collaborations involve consulting and advisory services as well as the development of specific inventions, and they can result in direct financial benefit to both individuals and institutions. Conflicts of interest reside in a situation itself, not in any behavior of members of a research team. Thus, researchers should disclose all conflicts of interest to their institutions so that the researchers and their work can be properly managed. They should also voluntarily disclose conflicts of interest in all publications and presentations resulting from the research. The committee believes that scientific institutions, including universities, research institutes, professional societies, and professional and lay journals, should embrace disclosure of conflicts of interest as an essential component of integrity in research.

Protection of Human Subjects in the Conduct of Research

The protection of individuals who volunteer to participate in research is essential to integrity in research. The ethical principles underlying such research have been elaborated on in international codes and have been integrated into national regulatory frameworks (in the United States, 45 C.F.R. § 46, 2001). Elements included in such frameworks pertain to the quality and importance of the science, its risks and benefits, fairness in the selection of subjects, and, above all, the voluntary participation and informed consent of subjects. To ensure the conformance of research efforts with these goals, institutions carry out extensive research subject protection programs. To be successful, such programs require high-level, functioning institutional review boards, knowledgeable investigators, ongoing performance assessment through monitoring and feedback, and educational programs (IOM, 2001). The IOM report Preserving Public Trust (IOM, 2001) focuses specifically on the important topic of research involving human subjects, and further discussion is not included here.

Humane Care of Animals in the Conduct of Research

The humane care of animals is essential for producing sound science and its social benefits. Researchers have a responsibility to engage in the humane care of animals in the conduct of research. This means evaluating the need for animals in any particular protocol, ensuring that research animals' basic needs for life are met prior to research, and carefully considering the benefits of the research to society or to animals versus the likely harms to any animals included as part of the research protocol. Procedures that minimize animal pain, suffering, and distress should be implemented. Research protocols involving animals must be reviewed and approved by properly constituted bodies, as required by law (Animal Welfare Act of 1966 [PL 89-544], inclusive of amendments passed in 1970 [PL 91-579], 1976 [PL 94-279], 1985 [PL 99-198], and 1990 [PL 101-624] and subsequent amendments) and professional standards (AAALAC, 2001; NRC, 1996).

Adherence to the Mutual Responsibilities Between Investigators and Their Research Teams

Adherence to the mutual responsibilities between investigators and members of their research teams refers to both scientific and interpersonal interactions. The research team might include other faculty members, colleagues (including coinvestigators), and trainees (undergraduate students, graduate and medical students, postdoctoral fellows), as well as employed staff (e.g., technicians, statisticians, study coordinators, nurses, animal handlers, and administrative personnel). The head of the research team should encourage all members of the team to achieve their career goals. The interpersonal interactions should reflect mutual respect among members of the team, fairness in assignment of responsibilities and effort, open and frequent communication, and accountability. In this regard, scientists should also conduct disputes professionally (Gunsalus, 1998). (The American Association of University Professors (AAUP) guidelines on academic freedom and professional ethics articulate the obligation of members of the academic community to root their statements in fact and to respect the opinions of others [AAUP, 1987, 1999].)

Mentoring and Advising

Mentor is often used interchangeably with faculty adviser . However, a mentor is more than a supervisor or an adviser (Bird, 2001; Swazey and Anderson, 1998). 3 An investigator or research adviser may or may not be a mentor. Some advisers may be accomplished researchers but do not have the time, training, or ability to be good mentors (NAS, 2000). For a trainee, “a mentoring relationship is a close, individualized relationship that develops over time between a graduate student (or other trainee) and a faculty member (or others) that includes both caring and guidance” (University of Michigan, 1999, p. 5). A successful mentoring relationship is based on mutual respect, trust, understanding, and empathy (NAS, 1997). Mentoring relationships can extend throughout all phases of a science career, and, as such, they are sometimes referred to as mentor-protégé or mentor-apprentice relationships, rather than mentor-trainee relationships.

The committee believes that mentor should be the dominant and usual role of the laboratory director or research advisor in regard to his or her trainee. With regard to such mentor-trainee relationships, responsibilities include a commitment to continuous education and guidance of trainees, appropriate delegation of responsibility, regular review and constructive appraisal of trainees, fair attribution of accomplishment and authorship, and career guidance, as well as help in creating opportunities for employment and funding. For the trainee, essential elements include respect for the mentor, loyalty to the research group, a strong commitment to science, dedication to the project, careful performance of experiments, precise and complete record keeping, accurate reporting of results, and a commitment to oral and written presentations and publication. It should be noted that most academic research institutions play a dual role. On the one hand, they are concerned with producing original research; on the other, with educating students. The two goals are compatible, but when they come in conflict, it is important that the educational needs of the students not be forgotten. If students are exploited, then they will learn by example that such behavior is acceptable.

• SUPPORT OF INTEGRITY BY THE RESEARCH INSTITUTION

The individual investigator and the laboratory or research unit carry out their functions in institutions that are responsible for the management and support of the research carried out within their domains. The institutions, in turn, are regulated by governmental and other bodies that impose rules and responsibilities (see Chapter 3 for further discussion). The vigor, resources, and attitudes with which institutions carry out their responsibilities will influence investigators' commitment and adherence to responsible research practices.

Provide Leadership in Support of Responsible Conduct of Research

It takes the leadership of an institution to promulgate a culture of responsible research. This involves the development of a vision for the research enterprise and a strategic plan. It is the responsibility of the institution leadership to develop programs to orient new researchers to institutional policies, rules, and guidelines; to sponsor opportunities for dialogue about new and emerging issues; and to sponsor continuing education about new policies and regulations as they are developed. Furthermore, institutional leaders have the responsibility to ensure that such programs are carried out, with appropriate delegation of responsibility and accountability and with adequate resources.

The observed actions of institutions in problem situations communicate as strongly (or perhaps more strongly) about responsible conduct as do any policies or programs. Institutional leaders (e.g., chancellor, president, dean, CEO) set the tone for the institutions with their own actions. Research leaders should set an example not only in their own research practices but also in their willingness to engage in dialogue about ethical questions that arise (Sigma Xi, 1999). McCabe and Pavela note that “faculty members who seek to instill a sense of social obligation without affirming personal virtues are planting trees without roots” (McCabe and Pavela, 1998, p.101).

Encourage Respect for Everyone Involved in the Research Enterprise

An environment that fosters competence and honest interactions among all participants in the investigative process supports the integrity of research. Institutions have many legally mandated policies that foster mutual respect and trust—for example, policies concerning harassment, occupational health and safety, fair employment practices, pay and benefits, protection of research subjects, exposure to ionizing radiation, and due process regarding allegations of research misconduct. State and local policies and guidelines governing research may be in effect as well. It is anticipated that through a process of self-assessment, institutions can identify issues and develop programs that further integrity in research (see Chapter 6 for further discussion). Fair enforcement of all institutional policies is a critical element of the institutional commitment to integrity in research. That is not enough, however.

Support Systems

Within the research institution, there can be multiple smaller units (e.g., departments, divisions within a department, research groups within a division). Within these institutional subunits, there will always be power differences between members of the group. Consequently, research institutions require support mechanisms—for example, ombudspersons—that research team members can turn to for help when they feel they are being treated unfairly. Institutions need to provide guidance and recourse to anyone with concerns about research integrity (e.g., a student who observes a lack of responsible conduct by a senior faculty member). Support systems should be accessible (multiple entry points for those with questions) and have a record of reaching objective, fact-based decisions untainted by personal bias or conflicts of interest (Gunsalus, 1993). Lack of recourse within the institution for those individuals who have concerns about possible misconduct will undermine efforts to foster a climate of integrity. Equally important to having support systems in place is the dissemination of information on how and where individuals may seek such support.

The ultimate goal for institutions should be to create a culture within which all persons on a research team can work effectively and realize their full potential.

Promote Productive Interactions Between Trainees and Mentors

Mentors play a special role in the development of new scientists. A mentor must consider the student's core interests and needs in preference to his or her own. Trainees and mentors are codependent and, at times, competitive. Trainees depend on their mentors for scientific education and training, for support, and, eventually, for career guidance and references. Mentors tend to be role models as well. Mentors depend on trainees for performing work and bringing fresh ideas and approaches to the research group. They can enhance the mentor's reputation as a teacher and as an investigator. Institutions should establish programs that foster productive relations between mentors and trainees, including training in mentoring and advising for faculty. Moreover, institutions should work to ensure that trainees are properly paid, receive reasonable benefits (including health insurance), and are protected from exploitation. Written guidelines, ombudspersons, and mutual evaluations can help to reduce problems and identify situations requiring remediation. As mentioned earlier in this chapter, the dual role academic research institutions play in both producing original research and educating students can be balanced, but when they come in conflict, educational interests of the student should take precedence.

Advocate Adherence to the Rules Regarding All Aspects of the Conduct of Research, Especially Research Involving Human Subjects and Animals

Effective advocacy by an institution of the rules involving the use of human subjects and animals in research involves much more than simply posting the relevant federal, state, and local regulations and providing “damage control” and formal sanctions when irregularities are discovered. At all levels of the institution, including the level of the dean, department chair, research group leader, and individual research group member, regular affirmation of the guiding principles underlying the rules is essential. The goal is to create an institutional climate such that anyone who violates these guiding principles through words or deeds is immediately made aware of the behavior and, when indicated, appropriately sanctioned.

Anticipate, Reveal, and Manage Individual and Institutional Conflicts of Interest

Research institutions must conduct their work in a manner that earns public trust. To do so, they must be sensitive to any conflict of interest that might affect or appear to affect their decisions and behavior in ways that could compromise their roles as trustworthy sources of information and policy advice or their obligations to ensure the protection of human research subjects. As research partnerships between industry and academic institutions continue to expand, with the promise of considerable public benefit, the management of real or perceived conflicts of interest in research requires that institutions have a written policy on such conflicts. The policy should apply to both institutions and individual investigators.

Institutional Conflicts of Interest

Institutions should have clearly stated policies and procedures by which they will guard against compromise by external influences. As with individual conflicts of interest, institutional leadership is not in the best position to determine whether a particular arrangement represents an unacceptable or manageable conflict of interest. Institutions should draw on independent reviews by external bodies and should have appropriate procedures for such reviews. Factors of concern include not only direct influences on institutional policy but also indirect influences on the use of resources, educational balance, and hiring of faculty, for example (AAU, 2001).

Institutional Responsibility for Investigator Conflicts of Interest

The policy on conflicts of interest should apply to individuals who are directly involved in the conduct, design, and review of research, including faculty, trainees, students, and administrators, and should clearly state their disclosure responsibilities. The policy should define conflicts of interest and should have means to convey an understanding of the term to the parties involved. It should delineate the activities and the levels and kinds of research-related financial interests that are and are not permissible, as well as those that require review and approval. The special circumstances associated with research involving human subjects should be specifically addressed. Beyond meeting their responsibility to ensure the dissemination and understanding of their policies, institutions should develop means to monitor compliance equitably. Detailed descriptions of institutional responsibilities in this area were recently reported by the Association of American Universities (AAU, 2001) and the Association of American Medical Colleges (AAMC, 2001), as described in Box 2-1 .

Definition of Institutional Conflict of Interest.

Arrange Timely and Thorough Inquiries and Investigations of Allegations of Scientific Misconduct and Apply Appropriate Sanctions

Every institution receiving federal funds for research and related activities must have in place policies and procedures for responding to allegations of research misconduct (42 C.F.R. § 50, §§ A, 1989; 45 C.F.R. § 689, 1996). Although the federal government imposes these requirements, the institutions must implement them. Their effectiveness depends on investigation of allegations of misconduct with vigor and fairness. The institution should embrace the notion that it is important to the quality and integrity of science that individuals report possible research misconduct. Means of protecting any individual who reports possible misconduct in good faith must be instituted.

In carrying out their responsibilities, institutions must ensure that faculty, students, and staff are properly informed of their rights and responsibilities. Those likely to receive allegations—for example, administrators, department chairs, and research group chiefs—must be fully informed of institutional provisions and trained in dealing with issues related to research conduct or misconduct. Mechanisms must be in place to protect the public's interest in the research record, the research subjects' health, and the financial interests of the institution, as well as to ensure notification of appropriate authorities. Clear lines of authority for management of the institution's response must exist, and, where indicated, appropriate sanctions should be applied or efforts should be made to protect or restore the reputations of innocent parties.

Offer Educational Opportunities Pertaining to Integrity in the Conduct of Research

Research institutions should provide students, faculty, and staff with educational opportunities related to the responsible conduct of research. These are mandatory for those involved in clinical research (NIH, 2000) and for recipients of Public Health Service training grants (NIH, 1989). These offerings should encourage open discussion of the values at stake and the ethical standards that promote responsible research practices. The core objective of such education is to increase participants' knowledge and sensitivity to the issues associated with integrity in research and to improve their ability to make ethical choices. It should give them an appreciation for the diversity of views that may be brought to bear on issues, inform them about the institutional rules and government regulations that apply to research, and instill in them the scientific community's expectations regarding proper research practice. Educational offerings should be flexible in their approach and be cognizant of normative differences among disciplines. Such programs should offer opportunities for the participants to explore the underlying values that shape the research enterprise and to analyze how those values are manifested in behaviors in different research environments

It is expected that effective educational programs will empower individual researchers, students, and staff in making responsible choices in the course of their research. Regular evaluation and improvement of the educational and behavioral effectiveness of these educational offerings should be a part of an institutional assessment. (See Chapter 5 for further discussion of education in the responsible conduct of research.)

Monitor and Evaluate the Institutional Environment Supporting Integrity in the Conduct of Research and Use This Knowledge for Continuous Quality Improvement

The main thrust of this report reflects the need for continuing attention toward sustaining and improving a culture of integrity in research. This requires diligent oversight by institutional management to ensure that the practices associated with integrity described above are carried out. It also requires examination of the policy-making process, the policies themselves, their execution, and the degree to which they are understood and adhered to by those affected. If researchers and administrators believe that the rules are excellent and that the institution applies them equitably, then the institutional commitment to integrity will be clear. Chapter 6 addresses ways to help identify those elements critical to establishment of the perception of moral commitment and determination of whether such commitments have been made.

The committee believes that integrity in research is essential for maintaining scientific excellence and keeping the public's trust. The concept of integrity in research cannot, however, be reduced to a one-line definition. For a scientist, integrity embodies above all the individual's commitment to intellectual honesty and personal responsibility. It is an aspect of moral character and experience. For an institution, it is a commitment to creating an environment that promotes responsible conduct by embracing standards of excellence, trustworthiness, and lawfulness and then assessing whether researchers and administrators perceive that an environment with high levels of integrity has been created. This chapter has described multiple practices that are most likely to promote responsible conduct. Individuals and institutions should use these practices with the goal of fostering a culture in which high ethical standards are the norm, ongoing professional development is encouraged, and public confidence in the scientific enterprise is preserved.

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• AAU (Association of American Universities). 2001. Report on Individual and Institutional Con flict of Interest . [Online] Available: http://www ​.aau.edu/research/conflict ​.html [Accessed January 31, 2002].
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• NIH (National Institutes of Health). 1989. Requirement for programs on the responsible conduct of research in National Research Service Award Institutional Training Programs, p. 1 . In: NIH Guide for Grants and Contracts, Vol. 18:1 , December 22, 1989. Rockville, MD: NIH.
• NIH. 2000. Required Education in the Protection of Human Research Participants NIH Guide for Grants and Contracts , June 5, 2000 (Revised August 25, 2000). [Online]. Available: http: //grants.nih.gov/grants/guide/notice-files/NOT-OD-00-039.html [Accessed December 10, 2001].
• NRC (National Research Council). 1996. Guide for the Care and Use of Laboratory Animals . Washington, DC: National Academy Press.
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Further discussion of moral character and behavior and the development of abilities that give rise to responsible conduct can be found in Chapter 5 .

See the section of Appendix D entitled Responsible Scientific Conduct for resources with case studies that can be used in a teaching setting to further illustrate the topics discussed here.

A special issue of Science and Engineering Ethics (7:451–640, 2001) is devoted to the relationship between mentoring and responsible conduct.

• Cite this Page National Research Council (US) and Institute of Medicine (US) Committee on Assessing Integrity in Research Environments. Integrity in Scientific Research: Creating an Environment That Promotes Responsible Conduct. Washington (DC): National Academies Press (US); 2002. 2, Integrity in Research.
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Harvard professor who studies dishonesty is accused of falsifying data

Juliana Kim

Francesca Gino has been teaching at Harvard Business School for 13 years. Maddie Meyer/Getty Images hide caption

Francesca Gino has been teaching at Harvard Business School for 13 years.

Francesca Gino, a prominent professor at Harvard Business School known for researching dishonesty and unethical behavior, has been accused of submitting work that contained falsified results.

Gino has authored dozens of captivating studies in the field of behavioral science — consulting for some of the world's biggest companies like Goldman Sachs and Google, as well as dispensing advice on news outlets, like The New York Times, The Wall Street Journal and even NPR .

But over the past two weeks, several people, including a colleague, came forward with claims that Gino tampered with data in at least four papers.

Harvard releases report detailing its ties to slavery, plans to issue reparations

Gino is currently on administrative leave. Harvard Business School declined to comment on when that decision was made as well as the allegations in general.

In a statement shared on LinkedIn , the professor said she was aware of the claims but did not deny or admit to any wrongdoing.

"As I continue to evaluate these allegations and assess my options, I am limited into what I can say publicly," Gino wrote on Saturday. "I want to assure you that I take them seriously and they will be addressed."

The scandal was first reported by The Chronicle of Higher Education earlier this month. According to the news outlet, over the past year, Harvard had been investigating a series of papers involving Gino.

University Of South Carolina President Resigns After Plagiarizing Part Of Speech

The university found that in a 2012 paper, it appeared someone had added and altered figures in its database, Max H. Bazerman, a Harvard Business School professor who collaborated with Gino in the past, told The Chronicle.

The study itself looked at whether honesty in tax and insurance paperwork differed between participants who were asked to sign truthfulness declarations at the top of the page versus at the bottom. The Proceedings of the National Academy of Sciences , which had published the research , has retracted it.

Shortly after the story, DataColada , a group of three investigators, came forward with similar accusations. After examining a number of Gino's works, the team said they found evidence of fraud spanning over a decade, most recently in 2020.

Acclaimed Harvard Scientist Is Arrested, Accused Of Lying About Ties To China

"Specifically, we wrote a report about four studies for which we had accumulated the strongest evidence of fraud. We believe that many more Gino-authored papers contain fake data. Perhaps dozens," DataColada wrote.

The group said they shared their concerns with Harvard Business School in 2021.

Gino has contributed to over a hundred academic articles around entrepreneurial success, promoting trust in the workforce and just last year published a study titled, "Case Study: What's the Right Career Move After a Public Failure?"

Academic Honesty Essay

Introduction, academic honesty, dishonest conduct, preventing academic dishonesty.

Lately, academic honesty has become a major issue among the elite in the academic environments. It can no longer be simply defined as the carrying of illegal materials into the exam rooms or copying someone else’s work. Indeed, with growth in technology like smart phones and emergence of the use of internet in research work has caused administrators in universities and colleges to extend the definition of academic honesty or dishonesty.

Academic honesty involves the students submitting work that is originally theirs and inclusion of the cited sources in their work. The academic community is generally aware that it is not possible for students to come up with their own original work and therefore, allow inclusion of other people’s work in form of direct quotes of paraphrases only if the original author is appropriately acknowledged. Academic honesty takes different forms and addresses in various aspects in schools and colleges.

Academic honesty is considered important because the results obtained from schools or colleges are referred to in future. Future employers refer to these documents when assessing the abilities and gifts of the students before actual employment.

Therefore, high levels of integrity should be adhered to in order to ensure quality reports and accurate assessment of the student’s abilities and potential (Vegh, 2009). Students commit academic dishonesty when they engage in activities that are classified in four general types; namely, cheating, dishonest conduct, plagiarism and collusion.

Cheating is the most ancient form of academic dishonesty known in history. It takes different forms whereby the rules and regulations governing formal or informal examinations are violated. For instance, copying other people’s work during examination, sharing one’s answers with another during examinations, or submission of other people’s work, as one’s own original work.

During examinations, invigilators are placed strategically in the exam room to monitor the behavior of students but some students attempt to share answers (Vegh, 2009). A student is not allowed to communicate to their fellow students in an exam room without the express permission of the invigilator and a violation of these rule amounts to cheating. Taking an examination on behalf of another student also amounts to cheating. Generally, cheating offers unfair advantage to the students involved over the rest.

Unfair advantage could also be meted on students when they commit dishonest conducts like stealing examination or answer keys from the instructor. Desperate times call for desperate measures and students are capable of doing anything to rescue their dreams of scooping first class honors.

Such cases have been reported severally and they can be classified as dishonest conduct (“What is Academic Dishonesty”, 1996, p.77). Further, students who try to change official academic results without following the procedures laid by the respective academic institutions commit dishonest conducts. Obtaining answers before the actual exam or altering records after certification leads to low academic standards.

Plagiarism is the recent form of violating academic honesty and defined as intellectual theft. The crime comes in when one makes use of another person’s findings, as if his/hers, without giving the due credit to the source. Plagiarism takes the form of stealing other people’s ideas or words and the form of use of other people’s work without crediting the source properly.

The sources mentioned here include articles from electronic journals, newspaper articles, published books, and even websites (Bouchard, 2010). The internet has become a source of information for research and the easy accessibility and convenience of the same provides a temptation to the students to copy and paste other people’s work.

However, it amounts to plagiarism and is classified as a violation of academic honesty. Though plagiarism can be either intentional or unintentional on the part of the student, it still amounts to academic dishonesty either way. Students should therefore be careful to ensure that their work is free of any form of plagiarism.

Academic institutions have come up with measures to curb the spread of academic dishonesty to maintain the credibility of their programs. Academic dishonesty leads to production of half-baked graduates who lower the standards of education hence that of the university (Staats, Hupp, & Hagley, 2008, p.360).

Students who commit academic dishonesty do not think on their own hence they do not develop the art of thinking which is critical for quality education. Ensuring enough spacing between students in the exam rooms and adhering to silence during exams reduces the rate of cheating in institutions. Instructors should also participate fully in ensuring that the work presented by students meets the set standards in respective academic institutions.

Instructors should be able to call the students and ask them questions regarding their submitted work to ensure that they wrote the work themselves. Technological developments also assist in fighting these vices in institutions. Software development in the computer science field has developed software able to detect plagiarism. This software, known as anti-plagiarism software, runs scans through the internet by comparing the submitted articles with various databases in the internet.

The sentences are compared and any of them found matching in a particular percentage is classified as plagiarism. Academic institutions use this software to ensure that students do not copy directly and they appropriately acknowledge their sources (Celik, 2009, p.275). In some institutions, violation of anti-plagiarism or academic honesty rules in general amount to punishment of different forms that in worst-case result to expulsion from these academic institutions.

The forms of academic dishonesty and methods of prevention discussed above are only general descriptions. However, they can be discussed further into way that is more specific and forms that would help improve the standards of education in academic institutions through policy research by the concerned institutions. Academic honesty is crucial to the growth of a country’s economy because integrity defines the character of future graduates.

Bouchard, K. (2010). Discipline in Schools: Technology tests academic honesty. McClatchy – Tribune Business News . Web.

Celik, C. (2009). Perceptions of University Students on Academic Honesty as Related to Gender, University Type, and Major in Turkey. Journal of American Academy of Business , 14(2), 271-278.

Staats, S., Hupp, J., & Hagley, A. (2008). Honesty and Heroes: A Positive Psychology View of Heroism and Academic Honesty. The Journal of Psychology , 42(4), 357-72.

Vegh, G. S. (2009). Academic honesty for a new generation. McClatchy – Tribune Business News. Web.

“What is Academic Dishonesty” (1996). In Teaching Resources Guide 1996-1997 (pp. 77-78). Irvine, CA: Instructional Resources Center, University of California.

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IvyPanda . "Academic Honesty Essay." August 1, 2022. https://ivypanda.com/essays/academic-honesty/.

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• Plagiarism: The Act of Copying Someone Else’s Words or Ideas
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March 19, 2024

The Dangers of Fast Science

Scientific research needs to slow down, not speed up, to produce trustworthy results

By Naomi Oreskes

Jarred Briggs

A theme at this year’s World Economic Forum (WEF) meeting in Davos, Switzerland, was the perceived need to “accelerate breakthroughs in research and technology.” Some of this framing was motivated by the climate emergency, some by the opportunities and challenges presented by generative artificial intelligence. Yet in various conversations, it seemed to be taken for granted that to address the world’s problems, scientific research needs to move faster.

The WEF mindset resonates with the Silicon Valley dictate—usually credited to Mark Zuckerberg—to move fast and break things. But what if the thing being broken is science? Or public trust?

The WEF meeting took place just a fortnight after Harvard University President Claudine Gay stepped down after complaints were made about her political science scholarship. Gay’s troubles came on the heels of the resignation of Stanford University President Marc Tessier-Lavigne, after an internal investigation concluded that his neuroscience research had “multiple problems” and “fell below customary standards of scientific rigor.” In response, Gay requested corrections to several of her papers; Tessier-Lavigne requested retraction of three of his. Although it may be impossible to determine just how widespread such problems really are, it’s hard to imagine that the spectacle of high-profile scholars correcting and retracting papers has not had a negative impact on public trust in science and perhaps in experts broadly.

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In recent years we’ve seen important papers, written by prominent scientists and published in prestigious journals, retracted because of questionable data or methods. In one interesting case, Frances H. Arnold of the California Institute of Technology, who shared the 2018 Nobel Prize in Chemistry, voluntarily retracted a paper when her lab was unable to replicate her results—but after the paper had been published. In an open apology, she stated that she was “a bit busy” when the paper was submitted and “did not do my job well.” Arnold’s honesty is admirable, but it raises a question: Are scholars at übercompetitive places such as Harvard, Stanford and Caltech rushing to publish rather than taking the time to do their work right?

It’s impossible to answer this question scientifically because there’s no scientific definition of what constitutes “rushing.” But there’s little doubt that we live in a culture where academics at leading universities are under tremendous pressure to produce results—and a lot of them—quickly.

The problem is not unique to the U.S. In Europe, formal research assessments—which are used to allocate future funding—have for years judged academic departments largely on the quantity of their output. A recent reform urging an emphasis on quality over quantity allowed that the existing system had created “counterincentives.”

Good science takes time. More than 50 years elapsed between the 1543 publication of Copernicus’s magnum opus, De Revolutionibus Orbium Coelestium (On the Revolutions of the Heavenly Spheres), and the broad scientific acceptance of the heliocentric model of the universe. Nearly a century passed between biochemist Friedrich Miescher’s identification of the DNA molecule and suggestion that it might be involved in inheritance and the elucidation of its double-helix structure in the 1950s. And it took just about half a century for geologists and geophysicists to accept geophysicist Alfred Wegener’s idea of continental drift.

There’s plenty of circumstantial evidence that scientists and other scholars are pushing results out far faster than they used to. Consider the sheer volume of academic papers being published these days. One recent study put the number at more than seven million a year, compared with fewer than a million as recently as 1980. Another study found 265 academic authors—two thirds of whom were in the medical and life sciences—who published a paper every five days on average.

Some of this growth is driven by more scientists and more co-authorship of papers, but the numbers also suggest that the research world has prioritized quantity over quality. Researchers may need to slow down—not speed up—if we are to produce knowledge worthy of trust.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American .

Can You Ever Really Escape Your Ex?

Your repeated attraction to a certain “type” may come down more to psychological comfort than a mysterious connection.

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Cool-but-not-too-cool artists; warm, friendly nerds or cold, unfriendly secret nerds; emotionally distant people; bossy, round-faced women; sensitive weirdos.

These are a few of the responses I got when I asked friends: “What’s your type?” No one seemed particularly surprised by the question, and a significant number responded without missing a beat. Nearly everyone gave me a highly specific answer. Some of them astutely described the kind of partner I really have seen them consistently attracted to; some, I thought, might just be trying to wrangle a motley crew of exes into a logical pattern. Either way, I got the sense that their romantic type was something they’d thought about a lot.

The notion of a “type”—a combination of physical, psychological, or other traits we’re repeatedly drawn to in a partner—feels entrenched in American culture. But it’s certainly not celebrated. Rather, “type” is often described as a vice, a pattern we fall into but shouldn’t. Cycling through versions of the same human template in one’s dating life, after all, sounds pretty futile. Saturday Night Live ’s spoof of reality dating shows in 2021 was called What’s Your Type? ; the joke, in large part, was that the bachelorette was inexplicably but consistently into men who were plainly terrible. Actual love-competition series don’t feel that far off from SNL ’s parody: Contestants frequently say things such as “He’s my type on paper” and “She’s not who I usually go for.” They may pursue the very person they aren’t initially pulled to—a hero’s journey that the audience cheers for—but many of them end up with their classic sort. In real life, coaches, influencers, therapists, and journalists exhort singles to “ date outside their type ”; clearly, the thinking goes, things haven’t been working out so far. (What’s that they say about doing the same thing over and over and expecting different results ?)

Evidently, many people have narratives about their own romantic preferences. But I wanted to know whether a “type” really does tend to guide our dating decisions—and, if it does, whether that truly is such a bad thing. Obviously, it’s unhealthy if you’re using it to stereotype, or to fetishize people’s physical qualities. But I thought there might be a way to reconcile being open-minded in who you date with recognizing that you respond for a reason to certain values or personality traits. So I spoke with some psychologists.

They told me that type is real, but maybe not in the way you think. It’s not a random collection of attributes that magically compel you; on the contrary, it could have roots you can trace clearly to the formative relationships of your past. And it might serve you to do so.

There do tend to be similarities among the people we date. In one set of 2017 studies , for instance, researchers found that subjects’ past partners were similar on measures including attractiveness, IQ, and educational aspirations. (That held true whether the relationship was casual or serious.) Another study in 2019 studied participants’ former and current partners, and found consistencies in the “Big Five” personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. Some research has even suggested that people have stable “types” when it comes to specific physical attributes such as eye color .

But this phenomenon can be explained, at least partly, by demographic stratification: We’re more likely to meet and spend time with people who are near us, and the people near us are likely to share certain characteristics. Elite-college students tend to date their classmates; astrophysicists might disproportionately pair up with other scientists. The authors of the 2017 study, for instance, found that when they controlled for the school their subjects were attending, the degree to which the participants had discrete preferences for some traits, such as IQ and academic ambitions, decreased significantly. Hypothetically, dating apps could help connect you to people who aren’t as likely to live in your neighborhood, hang out in the same groups, or show up to the same activities—but that doesn’t always mean people use them that way. Scholars have found that even beyond physical proximity, we’re still more likely to date people who are similar to us. They call this depressing finding “assortative mating”: People tend to couple up with those who match them on factors such as educational background and income.

Read: The mystery of partner ‘convergence’

That might appear a little different from the kinds of niche inclinations my friends told me about. Usually, when we talk about type, we’re implying a set of clear, specific, and personal tastes. But people’s stated preferences don’t always match their real ones. “People don’t know themselves super well,” Claudia Brumbaugh, a psychologist at Queens College, City University of New York, who has studied romantic proclivities, told me. And studies suggest that when it comes down to it, the traits people end up valuing in actual relationships are pretty basic, and remarkably consistent across cultures: kindness, intelligence, physical attractiveness. Brumbaugh guessed that if people are prompted to pick a type, they might think to name something unique: “artistic,” say. The traits that might actually draw them to a partner wouldn’t come to mind; they’re just too obvious.

And yet, we aren’t all attracted to the same kind, smart, and good-looking people all the time. There might be another reason you go for a certain type, aside from their proximity or similarity to you: They remind you of someone you’ve dated in the past.

Researchers have found that familiarity can increase our attraction to someone. That can happen with exposure to one person over time, but someone might also feel familiar because they’re similar to a person we’ve known before. Brumbaugh has studied this in the context of attachment theory, which describes how our past experiences can shape how we form and interpret new relationships. She’s found that when someone meets a person who resembles their ex-partner, they tend to feel more anxiously attached to them—more worried about rejection or getting their approval—than they’d typically be with a stranger. But they’re also likely to be less avoidant, meaning they’re more willing to talk and open up. “If someone reminds us, whether consciously or unconsciously, of a past partner,” Brumbaugh told me, “they’re going to feel more safe, more approachable.”

This might happen even if a new date reminds you of an ex you’d rather forget. Our early relationships create a framework for what romantic connection looks like: what emotions you’ll feel, what behaviors will be appreciated or dismissed, whether you can assume honesty or good intentions. So if you’ve experienced a partner who, say, makes you feel small, finding another who does the same might confirm your perception of how relationships work. Your repeated attraction to a certain “type,” then, may not be a mysterious connection but rather just psychological comfort. “Having a sense of control and predictability over our world” is hugely important, Brumbaugh told me. Perhaps so much so that it can feel easier to repeat bad patterns than to have our ideas about partners—and love, and relating to others—shattered.

In this sense, “type” is about not just the type of person you gravitate toward but also the type of relationship dynamic you fall into: how you communicate or show affection or trust. Matthew Johnson, a professor who studies couples at the University of Alberta, in Canada, has found that people’s relationships tend to have consistent qualities. In one study , he measured a handful of factors—including relationship satisfaction, sexual satisfaction and frequency, perceived instability, frequency of conflict, and how partners opened up to and expressed admiration for each other—in subjects’ past and current relationships, and found significant similarities. “We have kind of prototypical ways of relating to others,” Johnson told me—given that we tend to select similar mates and act in fairly stable ways, “you’re going to get this cocktail of a lot of consistency from one relationship to the next.”

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So perhaps if someone’s type is “sensitive weirdos,” that doesn’t necessarily mean they like to date only these people. It might just mean that they’ve dated a sensitive weirdo in the past, and that’s how they learned how to be in a relationship. Now they feel comfortable to some degree with people who share those traits—and their own habits might attract new sensitive weirdos, or vice versa. Those romantic reverberations can be dangerous, as Brumbaugh pointed out. Some studies back up the idea that a first love —even when it just seems like a silly teen romance—can set a bad benchmark, whether it’s because you don’t expect enough in subsequent relationships or because you expect too much. But maybe it’s not always an ex-partner’s bad qualities that drive a person to find someone similar; perhaps it’s nostalgia for the qualities they loved, Yoobin Park, a postdoctoral researcher at UC San Francisco, told me. And maybe, with repeated exposure, you can even learn how to respond gracefully to the traits you don’t love as much.

That’s not to say you should date the same kind of person over and over again. But perhaps it does mean that the answer isn’t to avoid doing so at all costs, either. What really matters is that you’re aware of the consistencies in whom you choose to partner with; you consider why they might exist, historically; and you’re honest about your own part in it. People tend to focus on the initial choice of a significant other, as if responsibility ends there, when, really, dating someone new might not transform the outcome at all. Whomever you pair up with, your flaws and insecurities will remain, to some degree. In all of your various romantic entanglements, the one absolute constant is you.